"""Stub file for tmp directory."""
from typing import Any, Dict, List, Optional, Tuple, Union

from PIL import Image
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from datetime import datetime
from datetime import datetime, timezone
from io import BytesIO
from matrice.docker_utils import pull_docker_image
from matrice_analytics.post_processing.core.config import BaseConfig
from matrice_common.utils import dependencies_check
from triton_model_manager import TritonModelManager
import GPUtil
import asyncio
import httpx
import logging
import numpy as np
import os
import psutil
import pytz
import shlex
import shutil
import subprocess
import threading
import time
import torch
import tritonclient.grpc as tritonclientclass
import tritonclient.http as tritonclientclass
import zipfile

# Constants
COCO_CLASSES: Any = ...  # From overall_inference_testing
logger: Any = ...  # From overall_inference_testing
BASE_PATH: Any = ...  # From triton_utils
TRITON_DOCKER_IMAGE: Any = ...  # From triton_utils

# Functions
# From overall_inference_testing
async def triton_async_benchmark(image_dir, num_requests = 100, output_report = 'master_benchmark_report_v1.md') -> Any: ...

# Classes
# From abstract_model_manager
class AbstractModelManager(ABC):
    """
    Abstract base class for model management.
    """

    def __init__(self, model_id: str, internal_server_type: str, internal_port: int, internal_host: str, action_tracker, num_model_instances: int = 1) -> None: ...
        """
        Initialize the model manager.
        
                Args:
                    model_id: ID of the model.
                    internal_server_type: Type of internal server.
                    internal_port: Internal port number.
                    internal_host: Internal host address.
                    action_tracker: Tracker for monitoring actions.
                    num_model_instances: Number of model instances to create.
        """

    def batch_inference(self, input1: List[Any], input2: Optional[List[Any]] = None, extra_params: Optional[Dict[str, Any]] = None, stream_key: Optional[str] = None, stream_info: Optional[Dict[str, Any]] = None, input_hash: Optional[str] = None) -> Tuple[List[Any], bool]: ...
        """
        Perform batch inference.
        """

    def get_model(self) -> Any: ...
        """
        Get a model instance for inference.
        """

    def inference(self, input1, input2: Optional[Any] = None, extra_params: Optional[Dict[str, Any]] = None, stream_key: Optional[str] = None, stream_info: Optional[Dict[str, Any]] = None, input_hash: Optional[str] = None) -> Tuple[Any, bool]: ...
        """
        Perform single inference.
        """


# From batch_manager
class BatchRequest:
    """
    Represents a single inference request in a batch
    """

    pass

# From batch_manager
class DynamicBatchManager:
    """
    Manages dynamic batching for inference requests
    """

    def __init__(self, batch_size: int, max_batch_wait_time: float, model_manager, post_processing_fn) -> None: ...
        """
        Initialize the dynamic batch manager.
        
        Args:
            batch_size: Maximum batch size for processing
            max_batch_wait_time: Maximum wait time for batching
            model_manager: Model manager for inference
            post_processing_fn: Function to apply post-processing
        """

    async def add_request(self, batch_request) -> Tuple[Any, Optional[Dict[str, Any]]]: ...
        """
        Add a request to the batch queue and process if needed
        """

    async def flush_queue(self) -> int: ...
        """
        Force process all remaining items in the batch queue.
        
                Returns:
                    Number of items processed
        """

    def get_stats(self) -> Dict[str, Any]: ...
        """
        Get statistics about the current batching state.
        """


# From triton_utils
class MatriceTritonServer:
    def __init__(self, action_tracker) -> None: ...

    def check_triton_docker_image(self) -> Any: ...
        """
        Check if docker image download is complete and wait for it to finish
        """

    def create_model_repository(self) -> Any: ...
        """
        Create the model repository directory structure
        """

    def download_model(self, model_version_dir) -> Any: ...
        """
        Download and extract the model files
        """

    def get_config_params(self) -> Any: ...

    def setup(self) -> Any: ...

    def start_server(self) -> Any: ...
        """
        Start the Triton Inference Server
        """

    def write_config_file(self, model_dir, max_batch_size = 0, num_model_instances = 1, image_size = [224, 224], num_classes = 10, input_data_type: str = 'TYPE_FP32', output_data_type: str = 'TYPE_FP32', dynamic_batching: bool = False, preferred_batch_size: list = [2, 4, 8], max_queue_delay_microseconds: int = 100, input_pinned_memory: bool = True, output_pinned_memory: bool = True, **kwargs) -> Any: ...
        """
        Write the model configuration file for Triton Inference Server
        """


# From triton_utils
class TritonInference:
    """
    Class for making Triton inference requests.
    """

    def __init__(self, server_type: str, model_id: str, internal_port: int = 80, internal_host: str = 'localhost') -> None: ...
        """
        Initialize Triton inference client.
        
                Args:
                    server_type: Type of server (grpc/rest)
                    model_id: ID of model to use
                    internal_port: Port number for internal API
                    internal_host: Hostname for internal API
        """

    async def async_inference(self, input_data) -> Any: ...
        """
        Make an asynchronous inference request.
        
                Args:
                    input_data: Input data as bytes
        
                Returns:
                    Model prediction as numpy array
        
                Raises:
                    Exception: If inference fails
        """

    def format_response(self, response) -> Dict[str, Any]: ...
        """
        Format model response for consistent logging.
        
                Args:
                    response: Raw model output
        
                Returns:
                    Formatted response dictionary
        """

    def inference(self, input_data) -> Any: ...
        """
        Make a synchronous inference request.
        
                Args:
                    input_data: Input data as bytes
        
                Returns:
                    Model prediction as numpy array
        
                Raises:
                    Exception: If inference fails
        """


from . import abstract_model_manager, batch_manager, overall_inference_testing, triton_utils