





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 						A_							(	_lowerCAmelCase	)		->					Dict:
 return EnvironmentCommand()



def 						A_							(	_lowerCAmelCase	)		->					Optional[Any]:
 return EnvironmentCommand(args.accelerate_config_file	)



class    A__  (			__snake_case     ):





      @staticmethod
      def 			__UpperCamelCase(					A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  parser.add_parser("env"  )
       download_parser.set_defaults(func=A_  )
       download_parser.add_argument(
           "--accelerate-config_file"			,				default=A_			,				help="The accelerate config file to use for the default values in the launching script."			,				)
       download_parser.set_defaults(func=A_  )





      def __init__(					self			,				A_			,				*A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  accelerate_config_file





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  "not installed"
       if is_safetensors_available():
        import safetensors

        UpperCamelCase      :   Dict			      =  safetensors.__version__
       elif importlib.util.find_spec("safetensors"  ) is not None:
        import safetensors

        UpperCamelCase      :   Union[str, Any]			      =  F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

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

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

        UpperCamelCase      :   int			      =  (
            "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]  )
            if isinstance(A_			,				A_  )
            else F"""\t{accelerate_config}"""
        )

       UpperCamelCase      :   Tuple			      =  "not installed"
       UpperCamelCase      :   Any			      =  "NA"
       if is_torch_available():
        import torch

        UpperCamelCase      :   Dict			      =  torch.__version__
        UpperCamelCase      :   Optional[int]			      =  torch.cuda.is_available()

       UpperCamelCase      :   Tuple			      =  "not installed"
       UpperCamelCase      :   Optional[int]			      =  "NA"
       if is_tf_available():
        import tensorflow as tf

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

       UpperCamelCase      :   Dict			      =  "not installed"
       UpperCamelCase      :   int			      =  "not installed"
       UpperCamelCase      :   int			      =  "not installed"
       UpperCamelCase      :   List[str]			      =  "NA"
       if is_flax_available():
        import flax
        import jax
        import jaxlib

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

       UpperCamelCase      :   List[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(A_  )  )

       return info





      @staticmethod
      def 			__UpperCamelCase(					A_  ):
       '''simple docstring'''




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







from random import shuffle

import tensorflow as tf
from numpy import array



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  int(_lowerCAmelCase	)
 assert noofclusters < len(_lowerCAmelCase	)

 # Find out the dimensionality
 UpperCamelCase      :   str			      =  len(vectors[0]	)

 # Will help select random centroids from among the available vectors
 UpperCamelCase      :   List[str]			      =  list(range(len(_lowerCAmelCase	)	)	)
 shuffle(_lowerCAmelCase	)

 # GRAPH OF COMPUTATION
 # We initialize a new graph and set it as the default during each run
 # of this algorithm. This ensures that as this function is called
 # multiple times, the default graph doesn't keep getting crowded with
 # unused ops and Variables from previous function calls.

 UpperCamelCase      :   Any			      =  tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  UpperCamelCase      :   Union[str, Any]			      =  tf.Session()

  ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

  ##First lets ensure we have a Variable vector for each centroid,
  ##initialized to one of the vectors from the available data points
  UpperCamelCase      :   Optional[int]			      =  [
      tf.Variable(vectors[vector_indices[i]]	) for i in range(_lowerCAmelCase	)
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float64"				,  [dim]	)
  UpperCamelCase      :   str			      =  []
  for centroid in centroids:
   cent_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  UpperCamelCase      :   List[str]			      =  [tf.Variable(0	) for i in range(len(_lowerCAmelCase	)	)]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  UpperCamelCase      :   Any			      =  tf.placeholder("int32"	)
  UpperCamelCase      :   Optional[int]			      =  []
  for assignment in assignments:
   cluster_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

  ##Now lets construct the node that will compute the mean
  # The placeholder for the input
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float"				,  [None, dim]	)
  # The Node/op takes the input and computes a mean along the 0th
  # dimension, i.e. the list of input vectors
  UpperCamelCase      :   List[str]			      =  tf.reduce_mean(_lowerCAmelCase				,  0	)

  ##Node for computing Euclidean distances
  # Placeholders for input
  UpperCamelCase      :   Optional[int]			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   Any			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   List[str]			      =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCAmelCase				,  _lowerCAmelCase	)				,  2	)	)	)

  ##This node will figure out which cluster to assign a vector to,
  ##based on Euclidean distances of the vector from the centroids.
  # Placeholder for input
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float"				,  [noofclusters]	)
  UpperCamelCase      :   str			      =  tf.argmin(_lowerCAmelCase				,  0	)

  ##INITIALIZING STATE VARIABLES

  ##This will help initialization of all Variables defined with respect
  ##to the graph. The Variable-initializer should be defined after
  ##all the Variables have been constructed, so that each of them
  ##will be included in the initialization.
  UpperCamelCase      :   Optional[Any]			      =  tf.initialize_all_variables()

  # Initialize all variables
  sess.run(_lowerCAmelCase	)

  ##CLUSTERING ITERATIONS

  # Now perform the Expectation-Maximization steps of K-Means clustering
  # iterations. To keep things simple, we will only do a set number of
  # iterations, instead of using a Stopping Criterion.
  UpperCamelCase      :   Any			      =  100
  for _ in range(_lowerCAmelCase	):
   ##EXPECTATION STEP
   ##Based on the centroid locations till last iteration, compute
   ##the _expected_ centroid assignments.
   # Iterate over each vector
   for vector_n in range(len(_lowerCAmelCase	)	):
    UpperCamelCase      :   Dict			      =  vectors[vector_n]
    # Compute Euclidean distance between this vector and each
    # centroid. Remember that this list cannot be named
    #'centroid_distances', since that is the input to the
    # cluster assignment node.
    UpperCamelCase      :   Optional[Any]			      =  [
        sess.run(_lowerCAmelCase				,  feed_dict={va: vect, va: sess.run(_lowerCAmelCase	)}	)
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    UpperCamelCase      :   Tuple			      =  sess.run(
        _lowerCAmelCase				,  feed_dict={centroid_distances: distances}	)
    # Now assign the value to the appropriate state variable
    sess.run(
        cluster_assigns[vector_n]				,  feed_dict={assignment_value: assignment}	)

   ##MAXIMIZATION STEP
   # Based on the expected state computed from the Expectation Step,
   # compute the locations of the centroids so as to maximize the
   # overall objective of minimizing within-cluster Sum-of-Squares
   for cluster_n in range(_lowerCAmelCase	):
    # Collect all the vectors assigned to this cluster
    UpperCamelCase      :   str			      =  [
        vectors[i]
        for i in range(len(_lowerCAmelCase	)	)
        if sess.run(assignments[i]	) == cluster_n
    ]
    # Compute new centroid location
    UpperCamelCase      :   Optional[int]			      =  sess.run(
        _lowerCAmelCase				,  feed_dict={mean_input: array(_lowerCAmelCase	)}	)
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n]				,  feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
  UpperCamelCase      :   Union[str, Any]			      =  sess.run(_lowerCAmelCase	)
  UpperCamelCase      :   Tuple			      =  sess.run(_lowerCAmelCase	)
  return centroids, assignments
