



'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			str       			=					int(_lowerCAmelCase			)
  assert noofclusters < len(_lowerCAmelCase			)

  # Find out the dimensionality
  __lowerCamelCase			:			Tuple       			=					len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  __lowerCamelCase			:			Dict       			=					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.

  __lowerCamelCase			:			Optional[Any]       			=					tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    __lowerCamelCase			:			Tuple       			=					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
    __lowerCamelCase			:			Optional[Any]       			=					[
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCAmelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    __lowerCamelCase			:			Optional[Any]       			=					tf.placeholder('float64'			,[dim]			)
    __lowerCamelCase			:			Optional[int]       			=					[]
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCAmelCase			,_lowerCAmelCase			)			)

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

    ##Node for computing Euclidean distances
    # Placeholders for input
    __lowerCamelCase			:			Union[str, Any]       			=					tf.placeholder('float'			,[dim]			)
    __lowerCamelCase			:			Optional[int]       			=					tf.placeholder('float'			,[dim]			)
    __lowerCamelCase			:			Dict       			=					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
    __lowerCamelCase			:			Optional[Any]       			=					tf.placeholder('float'			,[noofclusters]			)
    __lowerCamelCase			:			Optional[int]       			=					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.
    __lowerCamelCase			:			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.
    __lowerCamelCase			:			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			)			):
        __lowerCamelCase			:			List[Any]       			=					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.
        __lowerCamelCase			:			List[str]       			=					[
            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
        __lowerCamelCase			:			Optional[int]       			=					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
        __lowerCamelCase			:			Tuple       			=					[
            vectors[i]
            for i in range(len(_lowerCAmelCase			)			)
            if sess.run(assignments[i]			) == cluster_n
        ]
        # Compute new centroid location
        __lowerCamelCase			:			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
    __lowerCamelCase			:			Optional[Any]       			=					sess.run(_lowerCAmelCase			)
    __lowerCamelCase			:			Optional[Any]       			=					sess.run(_lowerCAmelCase			)
    return centroids, assignments











'''simple docstring'''





from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
   from fastapi import Body, FastAPI, HTTPException
   from fastapi.routing import APIRoute
   from pydantic import BaseModel
   from starlette.responses import JSONResponse
   from uvicorn import run

   _UpperCamelCase											=		True
except (ImportError, AttributeError):
   _UpperCamelCase											=		object

   def 			a_	(				*_lowerCAmelCase			,**_lowerCAmelCase			) -> Union[str, Any]:
     pass


   _UpperCamelCase											=		False


_UpperCamelCase											=		logging.get_logger('transformers-cli/serving')

def 			a_	(				_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			List[Any]       			=					pipeline(
      task=args.task			,model=args.model if args.model else None			,config=args.config			,tokenizer=args.tokenizer			,device=args.device			,)
  return ServeCommand(_lowerCAmelCase			,args.host			,args.port			,args.workers			)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42
  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  @staticmethod
  def 		_lowercase	(					_a :      ArgumentParser  )   ->   Optional[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					parser.add_parser(
        'serve'     ,      help='CLI tool to run inference requests through REST and GraphQL endpoints.'  )
    serve_parser.add_argument(
        '--task'     ,      type=_a     ,      choices=get_supported_tasks()     ,      help='The task to run the pipeline on'     ,      )
    serve_parser.add_argument('--host'     ,      type=_a     ,      default='localhost'     ,      help='Interface the server will listen on.'  )
    serve_parser.add_argument('--port'     ,      type=_a     ,      default=8888     ,      help='Port the serving will listen to.'  )
    serve_parser.add_argument('--workers'     ,      type=_a     ,      default=1     ,      help='Number of http workers'  )
    serve_parser.add_argument('--model'     ,      type=_a     ,      help='Model\'s name or path to stored model.'  )
    serve_parser.add_argument('--config'     ,      type=_a     ,      help='Model\'s config name or path to stored model.'  )
    serve_parser.add_argument('--tokenizer'     ,      type=_a     ,      help='Tokenizer name to use.'  )
    serve_parser.add_argument(
        '--device'     ,      type=_a     ,      default=-1     ,      help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,      )
    serve_parser.set_defaults(func=_a  )




  def __init__(					self :      int     ,      _a :      Pipeline     ,      _a :      str     ,      _a :      int     ,      _a :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					pipeline

    __lowerCamelCase			:			List[Any]       			=					host
    __lowerCamelCase			:			int       			=					port
    __lowerCamelCase			:			Tuple       			=					workers

    if not _serve_dependencies_installed:
      raise RuntimeError(
          'Using serve command requires FastAPI and uvicorn. '
          'Please install transformers with [serving]: pip install "transformers[serving]".'
          'Or install FastAPI and uvicorn separately.'  )
    else:
      logger.info(f'Serving model over {host}:{port}'  )
      __lowerCamelCase			:			Optional[Any]       			=					FastAPI(
          routes=[
              APIRoute(
                  '/'     ,      self.model_info     ,      response_model=_a     ,      response_class=_a     ,      methods=['GET']     ,      ),
              APIRoute(
                  '/tokenize'     ,      self.tokenize     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
              APIRoute(
                  '/detokenize'     ,      self.detokenize     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
              APIRoute(
                  '/forward'     ,      self.forward     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
          ]     ,      timeout=600     ,      )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   List[str]:
    run(self._app     ,      host=self.host     ,      port=self.port     ,      workers=self.workers  )




  def 		_lowercase	(					self :      Tuple  )   ->   int:
    return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )




  def 		_lowercase	(					self :      str     ,      _a :      str = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )  )   ->   Optional[int]:
    try:
      __lowerCamelCase			:			Dict       			=					self._pipeline.tokenizer.tokenize(_a  )

      if return_ids:
        __lowerCamelCase			:			int       			=					self._pipeline.tokenizer.convert_tokens_to_ids(_a  )
        return ServeTokenizeResult(tokens=_a     ,      tokens_ids=_a  )
      else:
        return ServeTokenizeResult(tokens=_a  )

    except Exception as e:
      raise HTTPException(status_code=500     ,      detail={'model': '', 'error': str(_a  )}  )




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[int] = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      )   ->   List[Any]:
    try:
      __lowerCamelCase			:			Dict       			=					self._pipeline.tokenizer.decode(_a     ,      _a     ,      _a  )
      return ServeDeTokenizeResult(model=''     ,      text=_a  )
    except Exception as e:
      raise HTTPException(status_code=500     ,      detail={'model': '', 'error': str(_a  )}  )




  async def 		_lowercase	(					self :      Dict     ,      _a :      Any=Body(_a     ,      embed=_a  )  )   ->   Optional[int]:

    # Check we don't have empty string
    if len(_a  ) == 0:
      return ServeForwardResult(output=[]     ,      attention=[]  )

    try:
      # Forward through the model
      __lowerCamelCase			:			Union[str, Any]       			=					self._pipeline(_a  )
      return ServeForwardResult(output=_a  )
    except Exception as e:
      raise HTTPException(500     ,      {'error': str(_a  )}  )






