

'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  int(__lowerCAmelCase     )
		assert noofclusters < len(__lowerCAmelCase     )

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

		# Will help select random centroids from among the available vectors
		snake_case__						:       List[Any]       						=  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.

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

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

				##Node for computing Euclidean distances
				# Placeholders for input
				snake_case__						:       Tuple       						=  tf.placeholder('''float'''   ,							[dim]     )
				snake_case__						:       int       						=  tf.placeholder('''float'''   ,							[dim]     )
				snake_case__						:       Optional[int]       						=  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
				snake_case__						:       Optional[int]       						=  tf.placeholder('''float'''   ,							[noofclusters]     )
				snake_case__						:       Dict       						=  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.
				snake_case__						:       List[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.
				snake_case__						:       List[str]       						=  1_00
				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     )     ):
								snake_case__						:       Optional[int]       						=  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.
								snake_case__						:       Union[str, 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
								snake_case__						:       str       						=  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
								snake_case__						:       Dict       						=  [
								    vectors[i]
								    for i in range(len(__lowerCAmelCase     )     )
								    if sess.run(assignments[i]     ) == cluster_n
								]
								# Compute new centroid location
								snake_case__						:       Union[str, Any]       						=  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
				snake_case__						:       List[str]       						=  sess.run(__lowerCAmelCase     )
				snake_case__						:       List[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

					lowerCAmelCase__						:       Any										=						True
except (ImportError, AttributeError):
					lowerCAmelCase__						:       Dict										=						object




					def 	_a       (				*__lowerCAmelCase		:      List[str]   ,							**__lowerCAmelCase		:      Tuple     ):

							"""simple docstring"""

							pass





					lowerCAmelCase__						:       str										=						False


lowerCAmelCase__						:       List[str]										=						logging.get_logger("""transformers-cli/serving""")




def 	_a       (				__lowerCAmelCase		:      Namespace     ):

		"""simple docstring"""

		snake_case__						:       Union[str, 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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@staticmethod
						def       __magic_name__							(							snake_case_					:      ArgumentParser  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  parser.add_parser(
								    '''serve'''			,			help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''  )
								serve_parser.add_argument(
								    '''--task'''			,			type=snake_case_			,			choices=get_supported_tasks()			,			help='''The task to run the pipeline on'''			,			)
								serve_parser.add_argument('''--host'''			,			type=snake_case_			,			default='''localhost'''			,			help='''Interface the server will listen on.'''  )
								serve_parser.add_argument('''--port'''			,			type=snake_case_			,			default=8_8_8_8			,			help='''Port the serving will listen to.'''  )
								serve_parser.add_argument('''--workers'''			,			type=snake_case_			,			default=1			,			help='''Number of http workers'''  )
								serve_parser.add_argument('''--model'''			,			type=snake_case_			,			help='''Model\'s name or path to stored model.'''  )
								serve_parser.add_argument('''--config'''			,			type=snake_case_			,			help='''Model\'s config name or path to stored model.'''  )
								serve_parser.add_argument('''--tokenizer'''			,			type=snake_case_			,			help='''Tokenizer name to use.'''  )
								serve_parser.add_argument(
								    '''--device'''			,			type=snake_case_			,			default=-1			,			help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''			,			)
								serve_parser.set_defaults(func=snake_case_  )







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      Pipeline			,			snake_case_					:      str			,			snake_case_					:      int			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  pipeline

								snake_case__						:       Tuple       						=  host
								snake_case__						:       Optional[Any]       						=  port
								snake_case__						:       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}"""  )
										snake_case__						:       str       						=  FastAPI(
										    routes=[
										        APIRoute(
										            '''/'''			,			self.model_info			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''GET''']			,			),
										        APIRoute(
										            '''/tokenize'''			,			self.tokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/detokenize'''			,			self.detokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/forward'''			,			self.forward			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										    ]			,			timeout=6_0_0			,			)







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								run(self._app			,			host=self.host			,			port=self.port			,			workers=self.workers  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      str = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[Any]       						=  self._pipeline.tokenizer.tokenize(snake_case_  )

										if return_ids:
												snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.convert_tokens_to_ids(snake_case_  )
												return ServeTokenizeResult(tokens=snake_case_			,			tokens_ids=snake_case_  )
										else:
												return ServeTokenizeResult(tokens=snake_case_  )

								except Exception as e:
										raise HTTPException(status_code=5_0_0			,			detail={'''model''': '''''', '''error''': str(snake_case_  )}  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[int] = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.decode(snake_case_			,			snake_case_			,			snake_case_  )
										return ServeDeTokenizeResult(model=''''''			,			text=snake_case_  )
								except Exception as e:
										raise HTTPException(status_code=5_0_0			,			detail={'''model''': '''''', '''error''': str(snake_case_  )}  )







						async def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]=Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



								if len(snake_case_  ) == 0:
										return ServeForwardResult(output=[]			,			attention=[]  )

								try:
										# Forward through the model
										snake_case__						:       Tuple       						=  self._pipeline(snake_case_  )
										return ServeForwardResult(output=snake_case_  )
								except Exception as e:
										raise HTTPException(5_0_0			,			{'''error''': str(snake_case_  )}  )



