

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_    :						Optional[Any]    		=      {
    "configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
    "processing_mgp_str": ["MgpstrProcessor"],
    "tokenization_mgp_str": ["MgpstrTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a_    :						Union[str, Any]    		=      [
          "MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MgpstrModel",
          "MgpstrPreTrainedModel",
          "MgpstrForSceneTextRecognition",
      ]

if TYPE_CHECKING:
      from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
      from .processing_mgp_str import MgpstrProcessor
      from .tokenization_mgp_str import MgpstrTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mgp_str import (
                MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
                MgpstrForSceneTextRecognition,
                MgpstrModel,
                MgpstrPreTrainedModel,
            )
else:
      import sys

      a_    :						List[Any]    		=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import random


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  float		,							_UpperCamelCase		:  bool = False	)		->							dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{i: [] for i in range(_UpperCamelCase	)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(_UpperCamelCase	)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(_UpperCamelCase	):
  for j in range(i + 1		,							_UpperCamelCase	):
   if random.random() < probability:
    graph[i].append(_UpperCamelCase	)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(_UpperCamelCase	)
 return graph


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							dict:
 '''simple docstring'''


 return {
     i: [j for j in range(_UpperCamelCase	) if i != j] for i in range(_UpperCamelCase	)
 }


if __name__ == "__main__":
      import doctest

      doctest.testmod()

