





from math import factorial






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   if successes > trials:
      raise ValueError("successes must be lower or equal to trials"   )
   if trials < 0 or successes < 0:
      raise ValueError("the function is defined for non-negative integers"   )
   if not isinstance(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_   ) or not isinstance(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_   ):
      raise ValueError("the function is defined for non-negative integers"   )
   if not 0 < prob < 1:
      raise ValueError("prob has to be in range of 1 - 0"   )
   a__	:						Optional[int]      		=  (prob**successes) * ((1 - prob) ** (trials - successes))
   # Calculate the binomial coefficient: n! / k!(n-k)!
   a__	:						Any      		=  float(factorial(SCREAMING_SNAKE_CASE_   )   )
   coefficient /= factorial(SCREAMING_SNAKE_CASE_   ) * factorial(trials - successes   )
   return probability * coefficient


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     print("""Probability of 2 successes out of 4 trails""")
     print("""with probability of 0.75 is:""", end=""" """)
     print(binomial_distribution(2, 4, 0.75))








# Copyright 2022 The HuggingFace Team and The OpenBMB 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

