text: # Copyright 2020-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions
  # are met:
  #  * Redistributions of source code must retain the above copyright
  #    notice, this list of conditions and the following disclaimer.
  #  * Redistributions in binary form must reproduce the above copyright
  #    notice, this list of conditions and the following disclaimer in the
  #    documentation and/or other materials provided with the distribution.
  #  * Neither the name of NVIDIA CORPORATION nor the names of its
  #    contributors may be used to endorse or promote products derived
  #    from this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
  # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
  # PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
  # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
  # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  try:
      from geventhttpclient import HTTPClient
      from geventhttpclient.url import URL
      import gevent
      import gevent.pool
      from urllib.parse import quote, quote_plus
      import rapidjson as json
      import numpy as np
      import struct
      import gzip, zlib
  except ModuleNotFoundError as error:
      raise RuntimeError(
          'The installation does not include http support. Specify \'http\' or \'all\' while installing the tritonclient package to include the support'
      ) from error
  
  from tritonclient.utils import *
  
  
  def _get_error(response):
      """
      Returns the InferenceServerException object if response
      indicates the error. If no error then return None
      """
      if response.status_code != 200:
          error_response = json.loads(response.read())
          return InferenceServerException(msg=error_response["error"])
      else:
          return None
  
  
  def _raise_if_error(response):
      """
      Raise InferenceServerException if received non-Success
      response from the server
      """
      error = _get_error(response)
      if error is not None:
          raise error
  
  
  def _get_query_string(query_params):
      params = []
      for key, value in query_params.items():
          if isinstance(value, list):
              for item in value:
                  params.append("%s=%s" %
                                (quote_plus(key), quote_plus(str(item))))
          else:
              params.append("%s=%s" % (quote_plus(key), quote_plus(str(value))))
      if params:
          return "&".join(params)
      return ''
  
  
  def _get_inference_request(inputs, request_id, outputs, sequence_id,
                             sequence_start, sequence_end, priority, timeout):
      infer_request = {}
      parameters = {}
      if request_id != "":
          infer_request['id'] = request_id
      if sequence_id != 0 and sequence_id != "":
          parameters['sequence_id'] = sequence_id
          parameters['sequence_start'] = sequence_start
          parameters['sequence_end'] = sequence_end
      if priority != 0:
          parameters['priority'] = priority
      if timeout is not None:
          parameters['timeout'] = timeout
  
      infer_request['inputs'] = [
          this_input._get_tensor() for this_input in inputs
      ]
      if outputs:
          infer_request['outputs'] = [
              this_output._get_tensor() for this_output in outputs
          ]
      else:
          # no outputs specified so set 'binary_data_output' True in the
          # request so that all outputs are returned in binary format
          parameters['binary_data_output'] = True
  
      if parameters:
          infer_request['parameters'] = parameters
  
      request_body = json.dumps(infer_request)
      json_size = len(request_body)
      binary_data = None
      for input_tensor in inputs:
          raw_data = input_tensor._get_binary_data()
          if raw_data is not None:
              if binary_data is not None:
                  binary_data += raw_data
              else:
                  binary_data = raw_data
  
      if binary_data is not None:
          request_body = struct.pack(
              '{}s{}s'.format(len(request_body), len(binary_data)),
              request_body.encode(), binary_data)
          return request_body, json_size
  
      return request_body, None
  
  
  class InferenceServerClient:
      """An InferenceServerClient object is used to perform any kind of
      communication with the InferenceServer using http protocol. None
      of the methods are thread safe. The object is intended to be used
      by a single thread and simultaneously calling different methods
      with different threads is not supported and will cause undefined
      behavior.
  
      Parameters
      ----------
      url : str
          The inference server name, port and optional base path 
          in the following format: host:port/<base-path>, e.g.
          'localhost:8000'.
  
      verbose : bool
          If True generate verbose output. Default value is False.
      concurrency : int
          The number of connections to create for this client.
          Default value is 1.
      connection_timeout : float
          The timeout value for the connection. Default value
          is 60.0 sec.
      network_timeout : float
          The timeout value for the network. Default value is
          60.0 sec
      max_greenlets : int
          Determines the maximum allowed number of worker greenlets
          for handling asynchronous inference requests. Default value
          is None, which means there will be no restriction on the
          number of greenlets created.
      ssl : bool
          If True, channels the requests to encrypted https scheme.
          Some improper settings may cause connection to prematurely
          terminate with an unsuccessful handshake. See
          `ssl_context_factory` option for using secure default
          settings. Default value for this option is False.
      ssl_options : dict
          Any options supported by `ssl.wrap_socket` specified as
          dictionary. The argument is ignored if 'ssl' is specified
          False.
      ssl_context_factory : SSLContext callable
          It must be a callbable that returns a SSLContext. Set to
          `gevent.ssl.create_default_context` to use contexts with
          secure default settings. This should most likely resolve
          connection issues in a secure way. The default value for
          this option is None which directly wraps the socket with
          the options provided via `ssl_options`. The argument is
          ignored if 'ssl' is specified False.
      insecure : bool
          If True, then does not match the host name with the certificate.
          Default value is False. The argument is ignored if 'ssl' is
          specified False.
  
      Raises
          ------
          Exception
              If unable to create a client.
  
      """
  
      def __init__(self,
                   url,
                   verbose=False,
                   concurrency=1,
                   connection_timeout=60.0,
                   network_timeout=60.0,
                   max_greenlets=None,
                   ssl=False,
                   ssl_options=None,
                   ssl_context_factory=None,
                   insecure=False):
          if url.startswith("http://") or url.startswith("https://"):
              raise_error("url should not include the scheme")
          scheme = "https://" if ssl else "http://"
          self._parsed_url = URL(scheme + url)
          self._base_uri = self._parsed_url.request_uri.rstrip('/')
          self._client_stub = HTTPClient.from_url(
              self._parsed_url,
              concurrency=concurrency,
              connection_timeout=connection_timeout,
              network_timeout=network_timeout,
              ssl_options=ssl_options,
              ssl_context_factory=ssl_context_factory,
              insecure=insecure)
          self._pool = gevent.pool.Pool(max_greenlets)
          self._verbose = verbose
  
      def __enter__(self):
          return self
  
      def __exit__(self, type, value, traceback):
          self.close()
  
      def __del__(self):
          self.close()
  
      def close(self):
          """Close the client. Any future calls to server
          will result in an Error.
  
          """
          self._pool.join()
          self._client_stub.close()
  
      def _get(self, request_uri, headers, query_params):
          """Issues the GET request to the server
  
           Parameters
          ----------
          request_uri: str
              The request URI to be used in GET request.
          headers: dict
              Additional HTTP headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          geventhttpclient.response.HTTPSocketPoolResponse
              The response from server.
          """
          if self._base_uri is not None:
              request_uri = self._base_uri + "/" + request_uri
  
          if query_params is not None:
              request_uri = request_uri + "?" + _get_query_string(query_params)
  
          if self._verbose:
              print("GET {}, headers {}".format(request_uri, headers))
  
          if headers is not None:
              response = self._client_stub.get(request_uri, headers=headers)
          else:
              response = self._client_stub.get(request_uri)
  
          if self._verbose:
              print(response)
  
          return response
  
      def _post(self, request_uri, request_body, headers, query_params):
          """Issues the POST request to the server
  
          Parameters
          ----------
          request_uri: str
              The request URI to be used in POST request.
          request_body: str
              The body of the request
          headers: dict
              Additional HTTP headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          geventhttpclient.response.HTTPSocketPoolResponse
              The response from server.
          """
          if self._base_uri is not None:
              request_uri = self._base_uri + "/" + request_uri
  
          if query_params is not None:
              request_uri = request_uri + "?" + _get_query_string(query_params)
  
          if self._verbose:
              print("POST {}, headers {}\n{}".format(request_uri, headers,
                                                     request_body))
  
          if headers is not None:
              response = self._client_stub.post(request_uri=request_uri,
                                                body=request_body,
                                                headers=headers)
          else:
              response = self._client_stub.post(request_uri=request_uri,
                                                body=request_body)
  
          if self._verbose:
              print(response)
  
          return response
  
      def is_server_live(self, headers=None, query_params=None):
          """Contact the inference server and get liveness.
  
          Parameters
          ----------
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          bool
              True if server is live, False if server is not live.
  
          Raises
          ------
          Exception
              If unable to get liveness.
  
          """
  
          request_uri = "v2/health/live"
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
  
          return response.status_code == 200
  
      def is_server_ready(self, headers=None, query_params=None):
          """Contact the inference server and get readiness.
  
          Parameters
          ----------
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          bool
              True if server is ready, False if server is not ready.
  
          Raises
          ------
          Exception
              If unable to get readiness.
  
          """
          request_uri = "v2/health/ready"
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
  
          return response.status_code == 200
  
      def is_model_ready(self,
                         model_name,
                         model_version="",
                         headers=None,
                         query_params=None):
          """Contact the inference server and get the readiness of specified model.
  
          Parameters
          ----------
          model_name: str
              The name of the model to check for readiness.
          model_version: str
              The version of the model to check for readiness. The default value
              is an empty string which means then the server will choose a version
              based on the model and internal policy.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          bool
              True if the model is ready, False if not ready.
  
          Raises
          ------
          Exception
              If unable to get model readiness.
  
          """
          if type(model_version) != str:
              raise_error("model version must be a string")
          if model_version != "":
              request_uri = "v2/models/{}/versions/{}/ready".format(
                  quote(model_name), model_version)
          else:
              request_uri = "v2/models/{}/ready".format(quote(model_name))
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
  
          return response.status_code == 200
  
      def get_server_metadata(self, headers=None, query_params=None):
          """Contact the inference server and get its metadata.
  
          Parameters
          ----------
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
  
          Returns
          -------
          dict
              The JSON dict holding the metadata.
  
          Raises
          ------
          InferenceServerException
              If unable to get server metadata.
  
          """
          request_uri = "v2"
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def get_model_metadata(self,
                             model_name,
                             model_version="",
                             headers=None,
                             query_params=None):
          """Contact the inference server and get the metadata for specified model.
  
          Parameters
          ----------
          model_name: str
              The name of the model
          model_version: str
              The version of the model to get metadata. The default value
              is an empty string which means then the server will choose
              a version based on the model and internal policy.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the metadata.
  
          Raises
          ------
          InferenceServerException
              If unable to get model metadata.
  
          """
          if type(model_version) != str:
              raise_error("model version must be a string")
          if model_version != "":
              request_uri = "v2/models/{}/versions/{}".format(
                  quote(model_name), model_version)
          else:
              request_uri = "v2/models/{}".format(quote(model_name))
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def get_model_config(self,
                           model_name,
                           model_version="",
                           headers=None,
                           query_params=None):
          """Contact the inference server and get the configuration for specified model.
  
          Parameters
          ----------
          model_name: str
              The name of the model
          model_version: str
              The version of the model to get configuration. The default value
              is an empty string which means then the server will choose
              a version based on the model and internal policy.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the model config.
  
          Raises
          ------
          InferenceServerException
              If unable to get model configuration.
  
          """
          if model_version != "":
              request_uri = "v2/models/{}/versions/{}/config".format(
                  quote(model_name), model_version)
          else:
              request_uri = "v2/models/{}/config".format(quote(model_name))
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def get_model_repository_index(self, headers=None, query_params=None):
          """Get the index of model repository contents
  
          Parameters
          ----------
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the model repository index.
  
          Raises
          ------
          InferenceServerException
              If unable to get the repository index.
  
          """
          request_uri = "v2/repository/index"
          response = self._post(request_uri=request_uri,
                                request_body="",
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def load_model(self,
                     model_name,
                     headers=None,
                     query_params=None,
                     config=None):
          """Request the inference server to load or reload specified model.
  
          Parameters
          ----------
          model_name : str
              The name of the model to be loaded.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
          config: str
              Optional JSON representation of a model config provided for
              the load request, if provided, this config will be used for
              loading the model.
  
          Raises
          ------
          InferenceServerException
              If unable to load the model.
  
          """
          request_uri = "v2/repository/models/{}/load".format(quote(model_name))
          load_request = {}
          if config is not None:
              if "parameters" not in load_request:
                  load_request["parameters"] = {}
              load_request["parameters"]["config"] = config
          response = self._post(request_uri=request_uri,
                                request_body=json.dumps(load_request),
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              print("Loaded model '{}'".format(model_name))
  
      def unload_model(self,
                       model_name,
                       headers=None,
                       query_params=None,
                       unload_dependents=False):
          """Request the inference server to unload specified model.
  
          Parameters
          ----------
          model_name : str
              The name of the model to be unloaded.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
          unload_dependents : bool
              Whether the dependents of the model should also be unloaded.
  
          Raises
          ------
          InferenceServerException
              If unable to unload the model.
  
          """
          request_uri = "v2/repository/models/{}/unload".format(quote(model_name))
          unload_request = {
              "parameters": {
                  "unload_dependents": unload_dependents
              }
          }
          response = self._post(request_uri=request_uri,
                                request_body=json.dumps(unload_request),
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              print("Loaded model '{}'".format(model_name))
  
      def get_inference_statistics(self,
                                   model_name="",
                                   model_version="",
                                   headers=None,
                                   query_params=None):
          """Get the inference statistics for the specified model name and
          version.
  
          Parameters
          ----------
          model_name : str
              The name of the model to get statistics. The default value is
              an empty string, which means statistics of all models will
              be returned.
          model_version: str
              The version of the model to get inference statistics. The
              default value is an empty string which means then the server
              will return the statistics of all available model versions.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the model inference statistics.
  
          Raises
          ------
          InferenceServerException
              If unable to get the model inference statistics.
  
          """
  
          if model_name != "":
              if type(model_version) != str:
                  raise_error("model version must be a string")
              if model_version != "":
                  request_uri = "v2/models/{}/versions/{}/stats".format(
                      quote(model_name), model_version)
              else:
                  request_uri = "v2/models/{}/stats".format(quote(model_name))
          else:
              request_uri = "v2/models/stats"
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def update_trace_settings(self,
                                model_name=None,
                                settings={},
                                headers=None,
                                query_params=None):
          """Update the trace settings for the specified model name, or
          global trace settings if model name is not given.
          Returns the trace settings after the update.
  
          Parameters
          ----------
          model_name : str
              The name of the model to update trace settings. Specifying None or
              empty string will update the global trace settings.
              The default value is None.
          settings: dict
              The new trace setting values. Only the settings listed will be
              updated. If a trace setting is listed in the dictionary with
              a value of 'None', that setting will be cleared.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the updated trace settings.
  
          Raises
          ------
          InferenceServerException
              If unable to update the trace settings.
  
          """
  
          if (model_name is not None) and (model_name != ""):
              request_uri = "v2/models/{}/trace/setting".format(quote(model_name))
          else:
              request_uri = "v2/trace/setting"
  
          response = self._post(request_uri=request_uri,
                                request_body=json.dumps(settings),
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def get_trace_settings(self,
                             model_name=None,
                             headers=None,
                             query_params=None):
          """Get the trace settings for the specified model name, or global trace
          settings if model name is not given
  
          Parameters
          ----------
          model_name : str
              The name of the model to get trace settings. Specifying None or
              empty string will return the global trace settings.
              The default value is None.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding the trace settings.
  
          Raises
          ------
          InferenceServerException
              If unable to get the trace settings.
  
          """
  
          if (model_name is not None) and (model_name != ""):
              request_uri = "v2/models/{}/trace/setting".format(quote(model_name))
          else:
              request_uri = "v2/trace/setting"
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def get_system_shared_memory_status(self,
                                          region_name="",
                                          headers=None,
                                          query_params=None):
          """Request system shared memory status from the server.
  
          Parameters
          ----------
          region_name : str
              The name of the region to query status. The default
              value is an empty string, which means that the status
              of all active system shared memory will be returned.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding system shared memory status.
  
          Raises
          ------
          InferenceServerException
              If unable to get the status of specified shared memory.
  
          """
          if region_name != "":
              request_uri = "v2/systemsharedmemory/region/{}/status".format(
                  quote(region_name))
          else:
              request_uri = "v2/systemsharedmemory/status"
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def register_system_shared_memory(self,
                                        name,
                                        key,
                                        byte_size,
                                        offset=0,
                                        headers=None,
                                        query_params=None):
          """Request the server to register a system shared memory with the
          following specification.
  
          Parameters
          ----------
          name : str
              The name of the region to register.
          key : str
              The key of the underlying memory object that contains the
              system shared memory region.
          byte_size : int
              The size of the system shared memory region, in bytes.
          offset : int
              Offset, in bytes, within the underlying memory object to
              the start of the system shared memory region. The default
              value is zero.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Raises
          ------
          InferenceServerException
              If unable to register the specified system shared memory.
  
          """
          request_uri = "v2/systemsharedmemory/region/{}/register".format(
              quote(name))
  
          register_request = {
              'key': key,
              'offset': offset,
              'byte_size': byte_size
          }
          request_body = json.dumps(register_request)
  
          response = self._post(request_uri=request_uri,
                                request_body=request_body,
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              print("Registered system shared memory with name '{}'".format(name))
  
      def unregister_system_shared_memory(self,
                                          name="",
                                          headers=None,
                                          query_params=None):
          """Request the server to unregister a system shared memory with the
          specified name.
  
          Parameters
          ----------
          name : str
              The name of the region to unregister. The default value is empty
              string which means all the system shared memory regions will be
              unregistered.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Raises
          ------
          InferenceServerException
              If unable to unregister the specified system shared memory region.
  
          """
          if name != "":
              request_uri = "v2/systemsharedmemory/region/{}/unregister".format(
                  quote(name))
          else:
              request_uri = "v2/systemsharedmemory/unregister"
  
          response = self._post(request_uri=request_uri,
                                request_body="",
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              if name != "":
                  print("Unregistered system shared memory with name '{}'".format(
                      name))
              else:
                  print("Unregistered all system shared memory regions")
  
      def get_cuda_shared_memory_status(self,
                                        region_name="",
                                        headers=None,
                                        query_params=None):
          """Request cuda shared memory status from the server.
  
          Parameters
          ----------
          region_name : str
              The name of the region to query status. The default
              value is an empty string, which means that the status
              of all active cuda shared memory will be returned.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Returns
          -------
          dict
              The JSON dict holding cuda shared memory status.
  
          Raises
          ------
          InferenceServerException
              If unable to get the status of specified shared memory.
  
          """
          if region_name != "":
              request_uri = "v2/cudasharedmemory/region/{}/status".format(
                  quote(region_name))
          else:
              request_uri = "v2/cudasharedmemory/status"
  
          response = self._get(request_uri=request_uri,
                               headers=headers,
                               query_params=query_params)
          _raise_if_error(response)
  
          content = response.read()
          if self._verbose:
              print(content)
  
          return json.loads(content)
  
      def register_cuda_shared_memory(self,
                                      name,
                                      raw_handle,
                                      device_id,
                                      byte_size,
                                      headers=None,
                                      query_params=None):
          """Request the server to register a system shared memory with the
          following specification.
  
          Parameters
          ----------
          name : str
              The name of the region to register.
          raw_handle : bytes
              The raw serialized cudaIPC handle in base64 encoding.
          device_id : int
              The GPU device ID on which the cudaIPC handle was created.
          byte_size : int
              The size of the cuda shared memory region, in bytes.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Raises
          ------
          InferenceServerException
              If unable to register the specified cuda shared memory.
  
          """
          request_uri = "v2/cudasharedmemory/region/{}/register".format(
              quote(name))
  
          register_request = {
              'raw_handle': {
                  'b64': raw_handle
              },
              'device_id': device_id,
              'byte_size': byte_size
          }
          request_body = json.dumps(register_request)
  
          response = self._post(request_uri=request_uri,
                                request_body=request_body,
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              print("Registered cuda shared memory with name '{}'".format(name))
  
      def unregister_cuda_shared_memory(self,
                                        name="",
                                        headers=None,
                                        query_params=None):
          """Request the server to unregister a cuda shared memory with the
          specified name.
  
          Parameters
          ----------
          name : str
              The name of the region to unregister. The default value is empty
              string which means all the cuda shared memory regions will be
              unregistered.
          headers: dict
              Optional dictionary specifying additional
              HTTP headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction
  
          Raises
          ------
          InferenceServerException
              If unable to unregister the specified cuda shared memory region.
  
          """
          if name != "":
              request_uri = "v2/cudasharedmemory/region/{}/unregister".format(
                  quote(name))
          else:
              request_uri = "v2/cudasharedmemory/unregister"
  
          response = self._post(request_uri=request_uri,
                                request_body="",
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
          if self._verbose:
              if name != "":
                  print("Unregistered cuda shared memory with name '{}'".format(
                      name))
              else:
                  print("Unregistered all cuda shared memory regions")
  
      @staticmethod
      def generate_request_body(inputs,
                                outputs=None,
                                request_id="",
                                sequence_id=0,
                                sequence_start=False,
                                sequence_end=False,
                                priority=0,
                                timeout=None):
          """Generate a request body for inference using the supplied 'inputs'
          requesting the outputs specified by 'outputs'.
  
          Parameters
          ----------
          inputs : list
              A list of InferInput objects, each describing data for a input
              tensor required by the model.
          outputs : list
              A list of InferRequestedOutput objects, each describing how the output
              data must be returned. If not specified all outputs produced
              by the model will be returned using default settings.
          request_id: str
              Optional identifier for the request. If specified will be returned
              in the response. Default value is an empty string which means no
              request_id will be used.
          sequence_id : int or str
              The unique identifier for the sequence being represented by the
              object. A value of 0 or "" means that the request does not
              belong to a sequence. Default is 0.
          sequence_start: bool
              Indicates whether the request being added marks the start of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          sequence_end: bool
              Indicates whether the request being added marks the end of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          priority : int
              Indicates the priority of the request. Priority value zero
              indicates that the default priority level should be used
              (i.e. same behavior as not specifying the priority parameter).
              Lower value priorities indicate higher priority levels. Thus
              the highest priority level is indicated by setting the parameter
              to 1, the next highest is 2, etc. If not provided, the server
              will handle the request using default setting for the model.
          timeout : int
              The timeout value for the request, in microseconds. If the request
              cannot be completed within the time the server can take a
              model-specific action such as terminating the request. If not
              provided, the server will handle the request using default setting
              for the model.
  
          Returns
          -------
          Bytes
              The request body of the inference.
          Int
              The byte size of the inference request header in the request body.
              Returns None if the whole request body constitutes the request header.
              
  
          Raises
          ------
          InferenceServerException
              If server fails to perform inference.
          """
          return _get_inference_request(inputs=inputs,
                                        request_id=request_id,
                                        outputs=outputs,
                                        sequence_id=sequence_id,
                                        sequence_start=sequence_start,
                                        sequence_end=sequence_end,
                                        priority=priority,
                                        timeout=timeout)
  
      @staticmethod
      def parse_response_body(response_body,
                              verbose=False,
                              header_length=None,
                              content_encoding=None):
          """Generate a InferResult object from the given 'response_body'
  
          Parameters
          ----------
          response_body : bytes
              The inference response from the server
          verbose : bool
              If True generate verbose output. Default value is False.
          header_length : int
              The length of the inference header if the header does not occupy
              the whole response body. Default value is None.
          content_encoding : string
              The encoding of the response body if it is compressed.
              Default value is None.
          
          Returns
          -------
          InferResult
              The InferResult object generated from the response body
          """
          return InferResult.from_response_body(response_body, verbose,
                                                header_length, content_encoding)
  
      def infer(self,
                model_name,
                inputs,
                model_version="",
                outputs=None,
                request_id="",
                sequence_id=0,
                sequence_start=False,
                sequence_end=False,
                priority=0,
                timeout=None,
                headers=None,
                query_params=None,
                request_compression_algorithm=None,
                response_compression_algorithm=None):
          """Run synchronous inference using the supplied 'inputs' requesting
          the outputs specified by 'outputs'.
  
          Parameters
          ----------
          model_name: str
              The name of the model to run inference.
          inputs : list
              A list of InferInput objects, each describing data for a input
              tensor required by the model.
          model_version: str
              The version of the model to run inference. The default value
              is an empty string which means then the server will choose
              a version based on the model and internal policy.
          outputs : list
              A list of InferRequestedOutput objects, each describing how the output
              data must be returned. If not specified all outputs produced
              by the model will be returned using default settings.
          request_id: str
              Optional identifier for the request. If specified will be returned
              in the response. Default value is an empty string which means no
              request_id will be used.
          sequence_id : int
              The unique identifier for the sequence being represented by the
              object. Default value is 0 which means that the request does not
              belong to a sequence.
          sequence_start: bool
              Indicates whether the request being added marks the start of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          sequence_end: bool
              Indicates whether the request being added marks the end of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          priority : int
              Indicates the priority of the request. Priority value zero
              indicates that the default priority level should be used
              (i.e. same behavior as not specifying the priority parameter).
              Lower value priorities indicate higher priority levels. Thus
              the highest priority level is indicated by setting the parameter
              to 1, the next highest is 2, etc. If not provided, the server
              will handle the request using default setting for the model.
          timeout : int
              The timeout value for the request, in microseconds. If the request
              cannot be completed within the time the server can take a
              model-specific action such as terminating the request. If not
              provided, the server will handle the request using default setting
              for the model.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request.
          query_params: dict
              Optional url query parameters to use in network
              transaction.
          request_compression_algorithm : str
              Optional HTTP compression algorithm to use for the request body on client side.
              Currently supports "deflate", "gzip" and None. By default, no
              compression is used.
          response_compression_algorithm : str
              Optional HTTP compression algorithm to request for the response body.
              Note that the response may not be compressed if the server does not
              support the specified algorithm. Currently supports "deflate",
              "gzip" and None. By default, no compression is requested.
  
          Returns
          -------
          InferResult
              The object holding the result of the inference.
  
          Raises
          ------
          InferenceServerException
              If server fails to perform inference.
          """
  
          request_body, json_size = _get_inference_request(
              inputs=inputs,
              request_id=request_id,
              outputs=outputs,
              sequence_id=sequence_id,
              sequence_start=sequence_start,
              sequence_end=sequence_end,
              priority=priority,
              timeout=timeout)
  
          if request_compression_algorithm == "gzip":
              if headers is None:
                  headers = {}
              headers["Content-Encoding"] = "gzip"
              request_body = gzip.compress(request_body)
          elif request_compression_algorithm == 'deflate':
              if headers is None:
                  headers = {}
              headers["Content-Encoding"] = "deflate"
              # "Content-Encoding: deflate" actually means compressing in zlib structure
              # https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Encoding
              request_body = zlib.compress(request_body)
  
          if response_compression_algorithm == "gzip":
              if headers is None:
                  headers = {}
              headers["Accept-Encoding"] = "gzip"
          elif response_compression_algorithm == 'deflate':
              if headers is None:
                  headers = {}
              headers["Accept-Encoding"] = "deflate"
  
          if json_size is not None:
              if headers is None:
                  headers = {}
              headers["Inference-Header-Content-Length"] = json_size
  
          if type(model_version) != str:
              raise_error("model version must be a string")
          if model_version != "":
              request_uri = "v2/models/{}/versions/{}/infer".format(
                  quote(model_name), model_version)
          else:
              request_uri = "v2/models/{}/infer".format(quote(model_name))
  
          response = self._post(request_uri=request_uri,
                                request_body=request_body,
                                headers=headers,
                                query_params=query_params)
          _raise_if_error(response)
  
          return InferResult(response, self._verbose)
  
      def async_infer(self,
                      model_name,
                      inputs,
                      model_version="",
                      outputs=None,
                      request_id="",
                      sequence_id=0,
                      sequence_start=False,
                      sequence_end=False,
                      priority=0,
                      timeout=None,
                      headers=None,
                      query_params=None,
                      request_compression_algorithm=None,
                      response_compression_algorithm=None):
          """Run asynchronous inference using the supplied 'inputs' requesting
          the outputs specified by 'outputs'. Even though this call is
          non-blocking, however, the actual number of concurrent requests to
          the server will be limited by the 'concurrency' parameter specified
          while creating this client. In other words, if the inflight
          async_infer exceeds the specified 'concurrency', the delivery of
          the exceeding request(s) to server will be blocked till the slot is
          made available by retrieving the results of previously issued requests.
  
          Parameters
          ----------
          model_name: str
              The name of the model to run inference.
          inputs : list
              A list of InferInput objects, each describing data for a input
              tensor required by the model.
          model_version: str
              The version of the model to run inference. The default value
              is an empty string which means then the server will choose
              a version based on the model and internal policy.
          outputs : list
              A list of InferRequestedOutput objects, each describing how the output
              data must be returned. If not specified all outputs produced
              by the model will be returned using default settings.
          request_id: str
              Optional identifier for the request. If specified will be returned
              in the response. Default value is 'None' which means no request_id
              will be used.
          sequence_id : int
              The unique identifier for the sequence being represented by the
              object. Default value is 0 which means that the request does not
              belong to a sequence.
          sequence_start: bool
              Indicates whether the request being added marks the start of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          sequence_end: bool
              Indicates whether the request being added marks the end of the
              sequence. Default value is False. This argument is ignored if
              'sequence_id' is 0.
          priority : int
              Indicates the priority of the request. Priority value zero
              indicates that the default priority level should be used
              (i.e. same behavior as not specifying the priority parameter).
              Lower value priorities indicate higher priority levels. Thus
              the highest priority level is indicated by setting the parameter
              to 1, the next highest is 2, etc. If not provided, the server
              will handle the request using default setting for the model.
          timeout : int
              The timeout value for the request, in microseconds. If the request
              cannot be completed within the time the server can take a
              model-specific action such as terminating the request. If not
              provided, the server will handle the request using default setting
              for the model.
          headers: dict
              Optional dictionary specifying additional HTTP
              headers to include in the request
          query_params: dict
              Optional url query parameters to use in network
              transaction.
          request_compression_algorithm : str
              Optional HTTP compression algorithm to use for the request body on client side.
              Currently supports "deflate", "gzip" and None. By default, no
              compression is used.
          response_compression_algorithm : str
              Optional HTTP compression algorithm to request for the response body.
              Note that the response may not be compressed if the server does not
              support the specified algorithm. Currently supports "deflate",
              "gzip" and None. By default, no compression is requested.
  
          Returns
          -------
          InferAsyncRequest object
              The handle to the asynchronous inference request.
  
          Raises
          ------
          InferenceServerException
              If server fails to issue inference.
          """
  
          def wrapped_post(request_uri, request_body, headers, query_params):
              return self._post(request_uri, request_body, headers, query_params)
  
          request_body, json_size = _get_inference_request(
              inputs=inputs,
              request_id=request_id,
              outputs=outputs,
              sequence_id=sequence_id,
              sequence_start=sequence_start,
              sequence_end=sequence_end,
              priority=priority,
              timeout=timeout)
  
          if request_compression_algorithm == "gzip":
              if headers is None:
                  headers = {}
              headers["Content-Encoding"] = "gzip"
              request_body = gzip.compress(request_body)
          elif request_compression_algorithm == 'deflate':
              if headers is None:
                  headers = {}
              headers["Content-Encoding"] = "deflate"
              # "Content-Encoding: deflate" actually means compressing in zlib structure
              # https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Encoding
              request_body = zlib.compress(request_body)
  
          if response_compression_algorithm == "gzip":
              if headers is None:
                  headers = {}
              headers["Accept-Encoding"] = "gzip"
          elif response_compression_algorithm == 'deflate':
              if headers is None:
                  headers = {}
              headers["Accept-Encoding"] = "deflate"
  
          if json_size is not None:
              if headers is None:
                  headers = {}
              headers["Inference-Header-Content-Length"] = json_size
  
          if type(model_version) != str:
              raise_error("model version must be a string")
          if model_version != "":
              request_uri = "v2/models/{}/versions/{}/infer".format(
                  quote(model_name), model_version)
          else:
              request_uri = "v2/models/{}/infer".format(quote(model_name))
  
          g = self._pool.apply_async(
              wrapped_post, (request_uri, request_body, headers, query_params))
  
          # Schedule the greenlet to run in this loop iteration
          g.start()
  
          # Relinquish control to greenlet loop. Using non-zero
          # value to ensure the control is transferred to the
          # event loop.
          gevent.sleep(0.01)
  
          if self._verbose:
              verbose_message = "Sent request"
              if request_id != "":
                  verbose_message = verbose_message + " '{}'".format(request_id)
              print(verbose_message)
  
          return InferAsyncRequest(g, self._verbose)
  
  
  class InferAsyncRequest:
      """An object of InferAsyncRequest class is used to describe
      a handle to an ongoing asynchronous inference request.
  
      Parameters
      ----------
      greenlet : gevent.Greenlet
          The greenlet object which will provide the results.
          For further details about greenlets refer
          http://www.gevent.org/api/gevent.greenlet.html.
  
      verbose : bool
          If True generate verbose output. Default value is False.
      """
  
      def __init__(self, greenlet, verbose=False):
          self._greenlet = greenlet
          self._verbose = verbose
  
      def get_result(self, block=True, timeout=None):
          """Get the results of the associated asynchronous inference.
          Parameters
          ----------
          block : bool
              If block is True, the function will wait till the
              corresponding response is received from the server.
              Default value is True.
          timeout : int
              The maximum wait time for the function. This setting is
              ignored if the block is set False. Default is None,
              which means the function will block indefinitely till
              the corresponding response is received.
  
          Returns
          -------
          InferResult
              The object holding the result of the async inference.
  
          Raises
          ------
          InferenceServerException
              If server fails to perform inference or failed to respond
              within specified timeout.
          """
  
          try:
              response = self._greenlet.get(block=block, timeout=timeout)
          except gevent.Timeout as e:
              raise_error("failed to obtain inference response")
  
          _raise_if_error(response)
          return InferResult(response, self._verbose)
  
  
  class InferInput:
      """An object of InferInput class is used to describe
      input tensor for an inference request.
  
      Parameters
      ----------
      name : str
          The name of input whose data will be described by this object
      shape : list
          The shape of the associated input.
      datatype : str
          The datatype of the associated input.
      """
  
      def __init__(self, name, shape, datatype):
          self._name = name
          self._shape = shape
          self._datatype = datatype
          self._parameters = {}
          self._data = None
          self._raw_data = None
  
      def name(self):
          """Get the name of input associated with this object.
  
          Returns
          -------
          str
              The name of input
          """
          return self._name
  
      def datatype(self):
          """Get the datatype of input associated with this object.
  
          Returns
          -------
          str
              The datatype of input
          """
          return self._datatype
  
      def shape(self):
          """Get the shape of input associated with this object.
  
          Returns
          -------
          list
              The shape of input
          """
          return self._shape
  
      def set_shape(self, shape):
          """Set the shape of input.
  
          Parameters
          ----------
          shape : list
              The shape of the associated input.
          """
          self._shape = shape
  
      def set_data_from_numpy(self, input_tensor, binary_data=True):
          """Set the tensor data from the specified numpy array for
          input associated with this object.
  
          Parameters
          ----------
          input_tensor : numpy array
              The tensor data in numpy array format
          binary_data : bool
              Indicates whether to set data for the input in binary format
              or explicit tensor within JSON. The default value is True,
              which means the data will be delivered as binary data in the
              HTTP body after the JSON object.
  
          Raises
          ------
          InferenceServerException
              If failed to set data for the tensor.
          """
          if not isinstance(input_tensor, (np.ndarray,)):
              raise_error("input_tensor must be a numpy array")
          dtype = np_to_triton_dtype(input_tensor.dtype)
          if self._datatype != dtype:
              raise_error(
                  "got unexpected datatype {} from numpy array, expected {}".
                  format(dtype, self._datatype))
          valid_shape = True
          if len(self._shape) != len(input_tensor.shape):
              valid_shape = False
          else:
              for i in range(len(self._shape)):
                  if self._shape[i] != input_tensor.shape[i]:
                      valid_shape = False
          if not valid_shape:
              raise_error(
                  "got unexpected numpy array shape [{}], expected [{}]".format(
                      str(input_tensor.shape)[1:-1],
                      str(self._shape)[1:-1]))
  
          self._parameters.pop('shared_memory_region', None)
          self._parameters.pop('shared_memory_byte_size', None)
          self._parameters.pop('shared_memory_offset', None)
  
          if not binary_data:
              self._parameters.pop('binary_data_size', None)
              self._raw_data = None
              if self._datatype == "BYTES":
                  self._data = []
                  try:
                      if input_tensor.size > 0:
                          for obj in np.nditer(input_tensor,
                                               flags=["refs_ok"],
                                               order='C'):
                              # We need to convert the object to string using utf-8,
                              # if we want to use the binary_data=False. JSON requires
                              # the input to be a UTF-8 string.
                              if input_tensor.dtype == np.object_:
                                  if type(obj.item()) == bytes:
                                      self._data.append(
                                          str(obj.item(), encoding='utf-8'))
                                  else:
                                      self._data.append(str(obj.item()))
                              else:
                                  self._data.append(
                                      str(obj.item(), encoding='utf-8'))
                  except UnicodeDecodeError:
                      raise_error(
                          f'Failed to encode "{obj.item()}" using UTF-8. Please use binary_data=True, if'
                          ' you want to pass a byte array.')
              else:
                  self._data = [val.item() for val in input_tensor.flatten()]
          else:
              self._data = None
              if self._datatype == "BYTES":
                  serialized_output = serialize_byte_tensor(input_tensor)
                  if serialized_output.size > 0:
                      self._raw_data = serialized_output.item()
                  else:
                      self._raw_data = b''
              else:
                  self._raw_data = input_tensor.tobytes()
              self._parameters['binary_data_size'] = len(self._raw_data)
  
      def set_shared_memory(self, region_name, byte_size, offset=0):
          """Set the tensor data from the specified shared memory region.
  
          Parameters
          ----------
          region_name : str
              The name of the shared memory region holding tensor data.
          byte_size : int
              The size of the shared memory region holding tensor data.
          offset : int
              The offset, in bytes, into the region where the data for
              the tensor starts. The default value is 0.
  
          """
          self._data = None
          self._raw_data = None
          self._parameters.pop('binary_data_size', None)
  
          self._parameters['shared_memory_region'] = region_name
          self._parameters['shared_memory_byte_size'] = byte_size
          if offset != 0:
              self._parameters['shared_memory_offset'].int64_param = offset
  
      def _get_binary_data(self):
          """Returns the raw binary data if available
  
          Returns
          -------
          bytes
              The raw data for the input tensor
          """
          return self._raw_data
  
      def _get_tensor(self):
          """Retrieve the underlying input as json dict.
  
          Returns
          -------
          dict
              The underlying tensor specification as dict
          """
          tensor = {
              'name': self._name,
              'shape': self._shape,
              'datatype': self._datatype
          }
          if self._parameters:
              tensor['parameters'] = self._parameters
  
          if self._parameters.get('shared_memory_region') is None and \
                  self._raw_data is None:
              if self._data is not None:
                  tensor['data'] = self._data
          return tensor
  
  
  class InferRequestedOutput:
      """An object of InferRequestedOutput class is used to describe a
      requested output tensor for an inference request.
  
      Parameters
      ----------
      name : str
          The name of output tensor to associate with this object.
      binary_data : bool
          Indicates whether to return result data for the output in
          binary format or explicit tensor within JSON. The default
          value is True, which means the data will be delivered as
          binary data in the HTTP body after JSON object. This field
          will be unset if shared memory is set for the output.
      class_count : int
          The number of classifications to be requested. The default
          value is 0 which means the classification results are not
          requested.
      """
  
      def __init__(self, name, binary_data=True, class_count=0):
          self._name = name
          self._parameters = {}
          if class_count != 0:
              self._parameters['classification'] = class_count
          self._binary = binary_data
          self._parameters['binary_data'] = binary_data
  
      def name(self):
          """Get the name of output associated with this object.
  
          Returns
          -------
          str
              The name of output
          """
          return self._name
  
      def set_shared_memory(self, region_name, byte_size, offset=0):
          """Marks the output to return the inference result in
          specified shared memory region.
  
          Parameters
          ----------
          region_name : str
              The name of the shared memory region to hold tensor data.
          byte_size : int
              The size of the shared memory region to hold tensor data.
          offset : int
              The offset, in bytes, into the region where the data for
              the tensor starts. The default value is 0.
  
          """
          if 'classification' in self._parameters:
              raise_error("shared memory can't be set on classification output")
          if self._binary:
              self._parameters['binary_data'] = False
  
          self._parameters['shared_memory_region'] = region_name
          self._parameters['shared_memory_byte_size'] = byte_size
          if offset != 0:
              self._parameters['shared_memory_offset'] = offset
  
      def unset_shared_memory(self):
          """Clears the shared memory option set by the last call to
          InferRequestedOutput.set_shared_memory(). After call to this
          function requested output will no longer be returned in a
          shared memory region.
          """
  
          self._parameters['binary_data'] = self._binary
          self._parameters.pop('shared_memory_region', None)
          self._parameters.pop('shared_memory_byte_size', None)
          self._parameters.pop('shared_memory_offset', None)
  
      def _get_tensor(self):
          """Retrieve the underlying input as json dict.
  
          Returns
          -------
          dict
              The underlying tensor as a dict
          """
          tensor = {'name': self._name}
          if self._parameters:
              tensor['parameters'] = self._parameters
          return tensor
  
  
  class InferResult:
      """An object of InferResult class holds the response of
      an inference request and provide methods to retrieve
      inference results.
  
      Parameters
      ----------
      response : geventhttpclient.response.HTTPSocketPoolResponse
          The inference response from the server
      verbose : bool
          If True generate verbose output. Default value is False.
      """
  
      def __init__(self, response, verbose):
          header_length = response.get('Inference-Header-Content-Length')
  
          # Internal class that simulate the interface of 'response'
          class DecompressedResponse:
  
              def __init__(self, decompressed_data):
                  self.decompressed_data_ = decompressed_data
                  self.offset_ = 0
  
              def read(self, length=-1):
                  if length == -1:
                      return self.decompressed_data_[self.offset_:]
                  else:
                      prev_offset = self.offset_
                      self.offset_ += length
                      return self.decompressed_data_[prev_offset:self.offset_]
  
          content_encoding = response.get('Content-Encoding')
          if content_encoding is not None:
              if content_encoding == "gzip":
                  response = DecompressedResponse(gzip.decompress(
                      response.read()))
              elif content_encoding == 'deflate':
                  response = DecompressedResponse(zlib.decompress(
                      response.read()))
          if header_length is None:
              content = response.read()
              if verbose:
                  print(content)
              try:
                  self._result = json.loads(content)
              except UnicodeDecodeError as e:
                  raise_error(
                      f'Failed to encode using UTF-8. Please use binary_data=True, if'
                      f' you want to pass a byte array. UnicodeError: {e}')
          else:
              header_length = int(header_length)
              content = response.read(length=header_length)
              if verbose:
                  print(content)
              self._result = json.loads(content)
  
              # Maps the output name to the index in buffer for quick retrieval
              self._output_name_to_buffer_map = {}
              # Read the remaining data off the response body.
              self._buffer = response.read()
              buffer_index = 0
              for output in self._result['outputs']:
                  parameters = output.get("parameters")
                  if parameters is not None:
                      this_data_size = parameters.get("binary_data_size")
                      if this_data_size is not None:
                          self._output_name_to_buffer_map[
                              output['name']] = buffer_index
                          buffer_index = buffer_index + this_data_size
  
      @classmethod
      def from_response_body(cls,
                             response_body,
                             verbose=False,
                             header_length=None,
                             content_encoding=None):
          """A class method to construct InferResult object
          from a given 'response_body'.
  
          Parameters
          ----------
          response_body : bytes
              The inference response from the server
          verbose : bool
              If True generate verbose output. Default value is False.
          header_length : int
              The length of the inference header if the header does not occupy
              the whole response body. Default value is None.
          content_encoding : string
              The encoding of the response body if it is compressed.
              Default value is None.
          
          Returns
          -------
          InferResult
              The InferResult object generated from the response body
          """
  
          # Internal class that simulate the interface of 'response'
          class Response:
  
              def __init__(self, response_body, header_length, content_encoding):
                  self.response_body_ = response_body
                  self.offset_ = 0
                  self.parameters_ = {
                      'Inference-Header-Content-Length': header_length,
                      'Content-Encoding': content_encoding
                  }
  
              def get(self, key):
                  return self.parameters_.get(key)
  
              def read(self, length=-1):
                  if length == -1:
                      return self.response_body_[self.offset_:]
                  else:
                      prev_offset = self.offset_
                      self.offset_ += length
                      return self.response_body_[prev_offset:self.offset_]
  
          return cls(Response(response_body, header_length, content_encoding),
                     verbose)
  
      def as_numpy(self, name):
          """Get the tensor data for output associated with this object
          in numpy format
  
          Parameters
          ----------
          name : str
              The name of the output tensor whose result is to be retrieved.
  
          Returns
          -------
          numpy array
              The numpy array containing the response data for the tensor or
              None if the data for specified tensor name is not found.
          """
          if self._result.get('outputs') is not None:
              for output in self._result['outputs']:
                  if output['name'] == name:
                      datatype = output['datatype']
                      has_binary_data = False
                      parameters = output.get("parameters")
                      if parameters is not None:
                          this_data_size = parameters.get("binary_data_size")
                          if this_data_size is not None:
                              has_binary_data = True
                              if this_data_size != 0:
                                  start_index = self._output_name_to_buffer_map[
                                      name]
                                  end_index = start_index + this_data_size
                                  if datatype == 'BYTES':
                                      # String results contain a 4-byte string length
                                      # followed by the actual string characters. Hence,
                                      # need to decode the raw bytes to convert into
                                      # array elements.
                                      np_array = deserialize_bytes_tensor(
                                          self._buffer[start_index:end_index])
                                  else:
                                      np_array = np.frombuffer(
                                          self._buffer[start_index:end_index],
                                          dtype=triton_to_np_dtype(datatype))
                              else:
                                  np_array = np.empty(0)
                      if not has_binary_data:
                          np_array = np.array(output['data'],
                                              dtype=triton_to_np_dtype(datatype))
                      np_array = np_array.reshape(output['shape'])
                      return np_array
          return None
  
      def get_output(self, name):
          """Retrieves the output tensor corresponding to the named ouput.
  
          Parameters
          ----------
          name : str
              The name of the tensor for which Output is to be
              retrieved.
  
          Returns
          -------
          Dict
              If an output tensor with specified name is present in
              the infer resonse then returns it as a json dict,
              otherwise returns None.
          """
          for output in self._result['outputs']:
              if output['name'] == name:
                  return output
  
          return None
  
      def get_response(self):
          """Retrieves the complete response
  
          Returns
          -------
          dict
              The underlying response dict.
          """
          return self._result
  