





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  return EnvironmentCommand()





def      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  return EnvironmentCommand(args.accelerate_config_file		)






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_					(UpperCAmelCase_				:   ArgumentParser)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =parser.add_parser("env")
   download_parser.set_defaults(func=UpperCAmelCase_)
   download_parser.add_argument(
       "--accelerate-config_file"       , default=UpperCAmelCase_       , help="The accelerate config file to use for the default values in the launching script."       , )
   download_parser.set_defaults(func=UpperCAmelCase_)

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   str       , *UpperCAmelCase_				:   Union[str, Any])       ->None:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =accelerate_config_file

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  ="not installed"
   if is_safetensors_available():
     import safetensors

     lowerCamelCase__:      Optional[Any]			  =safetensors.__version__
   elif importlib.util.find_spec("safetensors") is not None:
     import safetensors

     lowerCamelCase__:      Optional[Any]			  =F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

   lowerCamelCase__:      Optional[Any]			  ="not installed"
   lowerCamelCase__:      Any			  ="not found"
   if is_accelerate_available():
     import accelerate
     from accelerate.commands.config import default_config_file, load_config_from_file

     lowerCamelCase__:      Optional[int]			  =accelerate.__version__
     # Get the default from the config file.
     if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase_):
       lowerCamelCase__:      List[Any]			  =load_config_from_file(self._accelerate_config_file).to_dict()

     lowerCamelCase__:      Optional[Any]			  =(
         "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()])
         if isinstance(UpperCAmelCase_       , UpperCAmelCase_)
         else F"""\t{accelerate_config}"""
     )

   lowerCamelCase__:      Optional[Any]			  ="not installed"
   lowerCamelCase__:      str			  ="NA"
   if is_torch_available():
     import torch

     lowerCamelCase__:      str			  =torch.__version__
     lowerCamelCase__:      List[Any]			  =torch.cuda.is_available()

   lowerCamelCase__:      List[str]			  ="not installed"
   lowerCamelCase__:      Tuple			  ="NA"
   if is_tf_available():
     import tensorflow as tf

     lowerCamelCase__:      Tuple			  =tf.__version__
     try:
       # deprecated in v2.1
       lowerCamelCase__:      List[Any]			  =tf.test.is_gpu_available()
     except AttributeError:
       # returns list of devices, convert to bool
       lowerCamelCase__:      Union[str, Any]			  =bool(tf.config.list_physical_devices("GPU"))

   lowerCamelCase__:      int			  ="not installed"
   lowerCamelCase__:      Optional[int]			  ="not installed"
   lowerCamelCase__:      List[Any]			  ="not installed"
   lowerCamelCase__:      Any			  ="NA"
   if is_flax_available():
     import flax
     import jax
     import jaxlib

     lowerCamelCase__:      Optional[int]			  =flax.__version__
     lowerCamelCase__:      Tuple			  =jax.__version__
     lowerCamelCase__:      List[Any]			  =jaxlib.__version__
     lowerCamelCase__:      Dict			  =jax.lib.xla_bridge.get_backend().platform

   lowerCamelCase__:      Union[str, Any]			  ={
       "`transformers` version": version,
       "Platform": platform.platform(),
       "Python version": platform.python_version(),
       "Huggingface_hub version": huggingface_hub.__version__,
       "Safetensors version": F"""{safetensors_version}""",
       "Accelerate version": F"""{accelerate_version}""",
       "Accelerate config": F"""{accelerate_config_str}""",
       "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
       "Tensorflow version (GPU?)": F"""{tf_version} ({tf_cuda_available})""",
       "Flax version (CPU?/GPU?/TPU?)": F"""{flax_version} ({jax_backend})""",
       "Jax version": F"""{jax_version}""",
       "JaxLib version": F"""{jaxlib_version}""",
       "Using GPU in script?": "<fill in>",
       "Using distributed or parallel set-up in script?": "<fill in>",
   }

   print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
   print(self.format_dict(UpperCAmelCase_))

   return info







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_					(UpperCAmelCase_				:   str)       ->Union[str, Any]:



   '''simple docstring'''



   return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]) + "\n"








from typing import Any





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		list:







  """simple docstring"""





  _validation(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  # Creates data structures and fill initial step
  lowerCamelCase__:      dict			  ={}
  lowerCamelCase__:      dict			  ={}
  for state in states_space:
    lowerCamelCase__:      Optional[Any]			  =observations_space[0]
    lowerCamelCase__:      List[Any]			  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    lowerCamelCase__:      int			  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,    len(__a		)		):
    lowerCamelCase__:      Tuple			  =observations_space[o]
    lowerCamelCase__:      Optional[Any]			  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      lowerCamelCase__:      Tuple			  =""
      lowerCamelCase__:      Optional[Any]			  =-1
      for k_state in states_space:
        lowerCamelCase__:      int			  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          lowerCamelCase__:      List[str]			  =probability
          lowerCamelCase__:      int			  =k_state

            # Update probabilities and pointers dicts
      lowerCamelCase__:      Any			  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      lowerCamelCase__:      int			  =arg_max

    # The final observation
  lowerCamelCase__:      Any			  =observations_space[len(__a		) - 1]

  # argmax for given final observation
  lowerCamelCase__:      Optional[Any]			  =""
  lowerCamelCase__:      int			  =-1
  for k_state in states_space:
    lowerCamelCase__:      Tuple			  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      lowerCamelCase__:      List[Any]			  =probability
      lowerCamelCase__:      Dict			  =k_state
  lowerCamelCase__:      str			  =arg_max

  # Process pointers backwards
  lowerCamelCase__:      Union[str, Any]			  =last_state
  lowerCamelCase__:      List[str]			  =[]
  for o in range(len(__a		) - 1	,    -1	,    -1		):
    result.append(__a		)
    lowerCamelCase__:      Union[str, Any]			  =pointers[previous, observations_space[o]]
  result.reverse()

  return result





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_not_empty(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  _validate_lists(__a	,    __a		)
  _validate_dicts(
      __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]		):
    raise ValueError("There's an empty parameter"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_list(__a	,    "observations_space"		)
  _validate_list(__a	,    "states_space"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} must be a list"""
    raise ValueError(__a		)
  else:
    for x in _object:
      if not isinstance(__a	,    __a		):
        lowerCamelCase__:      str			  =F"""{var_name} must be a list of strings"""
        raise ValueError(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_dict(__a	,    "initial_probabilities"	,    __a		)
  _validate_nested_dict(__a	,    "transition_probabilities"		)
  _validate_nested_dict(__a	,    "emission_probabilities"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_dict(_object	,    __a	,    __a		)
  for x in _object.values():
    _validate_dict(__a	,    __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Optional[int]			  =F"""{var_name} must be a dict"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} all keys must be strings"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object.values()		):
    lowerCamelCase__:      Dict			  ="nested dictionary " if nested else ""
    lowerCamelCase__:      List[str]			  =F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(__a		)


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

     testmod()

