"""
Module for monitoring Together calls.
"""

import logging
import time
from opentelemetry.trace import SpanKind, Status, StatusCode
from opentelemetry.sdk.resources import SERVICE_NAME, TELEMETRY_SDK_NAME, DEPLOYMENT_ENVIRONMENT
from openlit.__helpers import (
    get_chat_model_cost,
    get_image_model_cost,
    handle_exception,
    response_as_dict,
    calculate_ttft,
    calculate_tbt,
    create_metrics_attributes,
    set_server_address_and_port
)
from openlit.semcov import SemanticConvetion

# Initialize logger for logging potential issues and operations
logger = logging.getLogger(__name__)

def completion(version, environment, application_name,
                tracer, pricing_info, trace_content, metrics, disable_metrics):
    """
    Generates a telemetry wrapper for chat completions to collect metrics.

    Args:
        version: Version of the monitoring package.
        environment: Deployment environment (e.g., production, staging).
        application_name: Name of the application using the Together AI API.
        tracer: OpenTelemetry tracer for creating spans.
        pricing_info: Information used for calculating the cost of Together AI usage.
        trace_content: Flag indicating whether to trace the actual content.

    Returns:
        A function that wraps the chat completions method to add telemetry.
    """

    class TracedSyncStream:
        """
        Wrapper for streaming responses to collect metrics and trace data.
        Wraps the response to collect message IDs and aggregated response.

        This class implements the '__aiter__' and '__anext__' methods that
        handle asynchronous streaming responses.

        This class also implements '__aenter__' and '__aexit__' methods that
        handle asynchronous context management protocol.
        """
        def __init__(
                self,
                wrapped,
                span,
                kwargs,
                server_address,
                server_port,
                **args,
            ):
            self.__wrapped__ = wrapped
            self._span = span
            # Placeholder for aggregating streaming response
            self._llmresponse = ""
            self._response_id = ""
            self._response_model = ""
            self._finish_reason = ""
            self._input_tokens = 0
            self._output_tokens = 0

            self._args = args
            self._kwargs = kwargs
            self._start_time = time.time()
            self._end_time = None
            self._timestamps = []
            self._ttft = 0
            self._tbt = 0
            self._server_address = server_address
            self._server_port = server_port

        def __enter__(self):
            self.__wrapped__.__enter__()
            return self

        def __exit__(self, exc_type, exc_value, traceback):
            self.__wrapped__.__exit__(exc_type, exc_value, traceback)

        def __iter__(self):
            return self

        def __getattr__(self, name):
            """Delegate attribute access to the wrapped object."""
            return getattr(self.__wrapped__, name)

        def __next__(self):
            try:
                chunk = self.__wrapped__.__next__()
                end_time = time.time()
                # Record the timestamp for the current chunk
                self._timestamps.append(end_time)

                if len(self._timestamps) == 1:
                    # Calculate time to first chunk
                    self._ttft = calculate_ttft(self._timestamps, self._start_time)

                chunked = response_as_dict(chunk)
                # Collect message IDs and aggregated response from events
                if (len(chunked.get('choices')) > 0 and ('delta' in chunked.get('choices')[0] and
                    'content' in chunked.get('choices')[0].get('delta'))):

                    content = chunked.get('choices')[0].get('delta').get('content')
                    if content:
                        self._llmresponse += content

                if chunked.get('usage'):
                    self._response_id = chunked.get('id')
                    self._response_model = chunked.get('model')
                    self._finish_reason = str(chunked.get('choices')[0].get('finish_reason'))
                    self._input_tokens = chunked.get('usage').get('prompt_tokens')
                    self._output_tokens = chunked.get('usage').get('completion_tokens')

                return chunk
            except StopIteration:
                # Handling exception ensure observability without disrupting operation
                try:
                    self._end_time = time.time()
                    if len(self._timestamps) > 1:
                        self._tbt = calculate_tbt(self._timestamps)

                    # Format 'messages' into a single string
                    message_prompt = self._kwargs.get("messages", "")
                    formatted_messages = []
                    for message in message_prompt:
                        role = message["role"]
                        content = message["content"]

                        if isinstance(content, list):
                            content_str_list = []
                            for item in content:
                                if item["type"] == "text":
                                    content_str_list.append(f'text: {item["text"]}')
                                elif (item["type"] == "image_url" and
                                      not item["image_url"]["url"].startswith("data:")):
                                    content_str_list.append(f'image_url: {item["image_url"]["url"]}')
                            content_str = ", ".join(content_str_list)
                            formatted_messages.append(f"{role}: {content_str}")
                        else:
                            formatted_messages.append(f"{role}: {content}")
                    prompt = "\n".join(formatted_messages)

                    request_model = self._kwargs.get("model", "gpt-4o")

                    # Calculate cost of the operation
                    cost = get_chat_model_cost(request_model,
                                                pricing_info, self._input_tokens,
                                                self._output_tokens)

                    # Set Span attributes (OTel Semconv)
                    self._span.set_attribute(TELEMETRY_SDK_NAME, "openlit")
                    self._span.set_attribute(SemanticConvetion.GEN_AI_OPERATION,
                                        SemanticConvetion.GEN_AI_OPERATION_TYPE_CHAT)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_SYSTEM,
                                        SemanticConvetion.GEN_AI_SYSTEM_TOGETHER)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_MODEL,
                                        request_model)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_SEED,
                                        self._kwargs.get("seed", ""))
                    self._span.set_attribute(SemanticConvetion.SERVER_PORT,
                                        self._server_port)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_FREQUENCY_PENALTY,
                                        self._kwargs.get("frequency_penalty", 0.0))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_MAX_TOKENS,
                                        self._kwargs.get("max_tokens", -1))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_PRESENCE_PENALTY,
                                        self._kwargs.get("presence_penalty", 0.0))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_STOP_SEQUENCES,
                                        self._kwargs.get("stop", []))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_TEMPERATURE,
                                        self._kwargs.get("temperature", 1.0))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_TOP_P,
                                        self._kwargs.get("top_p", 1.0))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_FINISH_REASON,
                                        [self._finish_reason])
                    self._span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_ID,
                                        self._response_id)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_MODEL,
                                        self._response_model)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_USAGE_INPUT_TOKENS,
                                        self._input_tokens)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_USAGE_OUTPUT_TOKENS,
                                        self._output_tokens)
                    self._span.set_attribute(SemanticConvetion.SERVER_ADDRESS,
                                        self._server_address)

                    if isinstance(self._llmresponse, str):
                        self._span.set_attribute(SemanticConvetion.GEN_AI_OUTPUT_TYPE,
                                        "text")
                    else:
                        self._span.set_attribute(SemanticConvetion.GEN_AI_OUTPUT_TYPE,
                                        "json")

                    # Set Span attributes (Extra)
                    self._span.set_attribute(DEPLOYMENT_ENVIRONMENT,
                                        environment)
                    self._span.set_attribute(SERVICE_NAME,
                                        application_name)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_USER,
                                        self._kwargs.get("user", ""))
                    self._span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_IS_STREAM,
                                        True)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_USAGE_TOTAL_TOKENS,
                                        self._input_tokens + self._output_tokens)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_USAGE_COST,
                                        cost)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_SERVER_TBT,
                                        self._tbt)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_SERVER_TTFT,
                                        self._ttft)
                    self._span.set_attribute(SemanticConvetion.GEN_AI_SDK_VERSION,
                                        version)
                    if trace_content:
                        self._span.add_event(
                            name=SemanticConvetion.GEN_AI_CONTENT_PROMPT_EVENT,
                            attributes={
                                SemanticConvetion.GEN_AI_CONTENT_PROMPT: prompt,
                            },
                        )
                        self._span.add_event(
                            name=SemanticConvetion.GEN_AI_CONTENT_COMPLETION_EVENT,
                            attributes={
                                SemanticConvetion.GEN_AI_CONTENT_COMPLETION: self._llmresponse,
                            },
                        )
                    self._span.set_status(Status(StatusCode.OK))

                    if disable_metrics is False:
                        attributes = create_metrics_attributes(
                            service_name=application_name,
                            deployment_environment=environment,
                            operation=SemanticConvetion.GEN_AI_OPERATION_TYPE_CHAT,
                            system=SemanticConvetion.GEN_AI_SYSTEM_TOGETHER,
                            request_model=request_model,
                            server_address=self._server_address,
                            server_port=self._server_port,
                            response_model=self._response_model,
                        )

                        metrics["genai_client_usage_tokens"].record(
                            self._input_tokens + self._output_tokens, attributes
                        )
                        metrics["genai_client_operation_duration"].record(
                            self._end_time - self._start_time, attributes
                        )
                        metrics["genai_server_tbt"].record(
                            self._tbt, attributes
                        )
                        metrics["genai_server_ttft"].record(
                            self._ttft, attributes
                        )
                        metrics["genai_requests"].add(1, attributes)
                        metrics["genai_completion_tokens"].add(self._output_tokens, attributes)
                        metrics["genai_prompt_tokens"].add(self._input_tokens, attributes)
                        metrics["genai_cost"].record(cost, attributes)

                except Exception as e:
                    handle_exception(self._span, e)
                    logger.error("Error in trace creation: %s", e)
                finally:
                    self._span.end()
                raise
    def wrapper(wrapped, instance, args, kwargs):
        """
        Wraps the 'chat.completions' API call to add telemetry.

        This collects metrics such as execution time, cost, and token usage, and handles errors
        gracefully, adding details to the trace for observability.

        Args:
            wrapped: The original 'chat.completions' method to be wrapped.
            instance: The instance of the class where the original method is defined.
            args: Positional arguments for the 'chat.completions' method.
            kwargs: Keyword arguments for the 'chat.completions' method.

        Returns:
            The response from the original 'chat.completions' method.
        """

        # Check if streaming is enabled for the API call
        streaming = kwargs.get("stream", False)
        server_address, server_port = set_server_address_and_port(instance, "api.together.xyz", 443)
        request_model = kwargs.get("model", "gpt-4o")

        span_name = f"{SemanticConvetion.GEN_AI_OPERATION_TYPE_CHAT} {request_model}"

        # pylint: disable=no-else-return
        if streaming:
            # Special handling for streaming response to accommodate the nature of data flow
            awaited_wrapped = wrapped(*args, **kwargs)
            span = tracer.start_span(span_name, kind=SpanKind.CLIENT)

            return TracedSyncStream(awaited_wrapped, span, kwargs, server_address, server_port)

        # Handling for non-streaming responses
        else:
            with tracer.start_as_current_span(span_name, kind= SpanKind.CLIENT) as span:
                start_time = time.time()
                response = wrapped(*args, **kwargs)
                end_time = time.time()

                response_dict = response_as_dict(response)

                try:
                    # Format 'messages' into a single string
                    message_prompt = kwargs.get("messages", "")
                    formatted_messages = []
                    for message in message_prompt:
                        role = message["role"]
                        content = message["content"]

                        if isinstance(content, list):
                            content_str = ", ".join(
                                f'{item["type"]}: {item["text"] if "text" in item else item["image_url"]}'
                                if "type" in item else f'text: {item["text"]}'
                                for item in content
                            )
                            formatted_messages.append(f"{role}: {content_str}")
                        else:
                            formatted_messages.append(f"{role}: {content}")
                    prompt = "\n".join(formatted_messages)

                    input_tokens = response_dict.get('usage').get('prompt_tokens')
                    output_tokens = response_dict.get('usage').get('completion_tokens')

                    # Calculate cost of the operation
                    cost = get_chat_model_cost(request_model,
                                                pricing_info, input_tokens,
                                                output_tokens)

                    # Set base span attribues (OTel Semconv)
                    span.set_attribute(TELEMETRY_SDK_NAME, "openlit")
                    span.set_attribute(SemanticConvetion.GEN_AI_OPERATION,
                                        SemanticConvetion.GEN_AI_OPERATION_TYPE_CHAT)
                    span.set_attribute(SemanticConvetion.GEN_AI_SYSTEM,
                                        SemanticConvetion.GEN_AI_SYSTEM_TOGETHER)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_MODEL,
                                        request_model)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_SEED,
                                        kwargs.get("seed", ""))
                    span.set_attribute(SemanticConvetion.SERVER_PORT,
                                        server_port)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_FREQUENCY_PENALTY,
                                        kwargs.get("frequency_penalty", 0.0))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_MAX_TOKENS,
                                        kwargs.get("max_tokens", -1))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_PRESENCE_PENALTY,
                                        kwargs.get("presence_penalty", 0.0))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_STOP_SEQUENCES,
                                        kwargs.get("stop", []))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_TEMPERATURE,
                                        kwargs.get("temperature", 1.0))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_TOP_P,
                                        kwargs.get("top_p", 1.0))
                    span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_ID,
                                        response_dict.get("id"))
                    span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_MODEL,
                                        response_dict.get('model'))
                    span.set_attribute(SemanticConvetion.GEN_AI_USAGE_INPUT_TOKENS,
                                        input_tokens)
                    span.set_attribute(SemanticConvetion.GEN_AI_USAGE_OUTPUT_TOKENS,
                                        output_tokens)
                    span.set_attribute(SemanticConvetion.SERVER_ADDRESS,
                                        server_address)

                    # Set base span attribues (Extras)
                    span.set_attribute(DEPLOYMENT_ENVIRONMENT,
                                        environment)
                    span.set_attribute(SERVICE_NAME,
                                        application_name)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_USER,
                                        kwargs.get("user", ""))
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_IS_STREAM,
                                        False)
                    span.set_attribute(SemanticConvetion.GEN_AI_USAGE_TOTAL_TOKENS,
                                        input_tokens + output_tokens)
                    span.set_attribute(SemanticConvetion.GEN_AI_USAGE_COST,
                                        cost)
                    span.set_attribute(SemanticConvetion.GEN_AI_SERVER_TTFT,
                                        end_time - start_time)
                    span.set_attribute(SemanticConvetion.GEN_AI_SDK_VERSION,
                                        version)
                    if trace_content:
                        span.add_event(
                            name=SemanticConvetion.GEN_AI_CONTENT_PROMPT_EVENT,
                            attributes={
                                SemanticConvetion.GEN_AI_CONTENT_PROMPT: prompt,
                            },
                        )

                    for i in range(kwargs.get('n',1)):
                        span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_FINISH_REASON,
                                           [str(response_dict.get('choices')[i].get('finish_reason'))])
                        if trace_content:
                            span.add_event(
                                name=SemanticConvetion.GEN_AI_CONTENT_COMPLETION_EVENT,
                                attributes={
                                    # pylint: disable=line-too-long
                                    SemanticConvetion.GEN_AI_CONTENT_COMPLETION: str(response_dict.get('choices')[i].get('message').get('content')),
                                },
                            )
                        if kwargs.get('tools'):
                            span.set_attribute(SemanticConvetion.GEN_AI_TOOL_CALLS,
                                            str(response_dict.get('choices')[i].get('message').get('tool_calls')))

                        if isinstance(response_dict.get('choices')[i].get('message').get('content'), str):
                            span.set_attribute(SemanticConvetion.GEN_AI_OUTPUT_TYPE,
                                            "text")
                        elif response_dict.get('choices')[i].get('message').get('content') is not None:
                            span.set_attribute(SemanticConvetion.GEN_AI_OUTPUT_TYPE,
                                            "json")

                    span.set_status(Status(StatusCode.OK))

                    if disable_metrics is False:
                        attributes = create_metrics_attributes(
                            service_name=application_name,
                            deployment_environment=environment,
                            operation=SemanticConvetion.GEN_AI_OPERATION_TYPE_CHAT,
                            system=SemanticConvetion.GEN_AI_SYSTEM_TOGETHER,
                            request_model=request_model,
                            server_address=server_address,
                            server_port=server_port,
                            response_model=response_dict.get('model'),
                        )

                        metrics["genai_client_usage_tokens"].record(
                            input_tokens + output_tokens, attributes
                        )
                        metrics["genai_client_operation_duration"].record(
                            end_time - start_time, attributes
                        )
                        metrics["genai_server_ttft"].record(
                            end_time - start_time, attributes
                        )
                        metrics["genai_requests"].add(1, attributes)
                        metrics["genai_completion_tokens"].add(output_tokens, attributes)
                        metrics["genai_prompt_tokens"].add(input_tokens, attributes)
                        metrics["genai_cost"].record(cost, attributes)

                    # Return original response
                    return response

                except Exception as e:
                    handle_exception(span, e)
                    logger.error("Error in trace creation: %s", e)

                    # Return original response
                    return response

    return wrapper

def image_generate(version, environment, application_name,
                   tracer, pricing_info, trace_content, metrics, disable_metrics):
    """
    Generates a telemetry wrapper for image generation to collect metrics.
    
    Args:
        version: Version of the monitoring package.
        environment: Deployment environment (e.g., production, staging).
        application_name: Name of the application using the Together AI API.
        tracer: OpenTelemetry tracer for creating spans.
        pricing_info: Information used for calculating the cost of Together AI image generation.
        trace_content: Flag indicating whether to trace the input prompt and generated images.
    
    Returns:
        A function that wraps the image generation method to add telemetry.
    """

    def wrapper(wrapped, instance, args, kwargs):
        """
        Wraps the 'images.generate' API call to add telemetry.

        This collects metrics such as execution time, cost, and handles errors
        gracefully, adding details to the trace for observability.

        Args:
            wrapped: The original 'images.generate' method to be wrapped.
            instance: The instance of the class where the original method is defined.
            args: Positional arguments for the 'images.generate' method.
            kwargs: Keyword arguments for the 'images.generate' method.

        Returns:
            The response from the original 'images.generate' method.
        """

        server_address, server_port = set_server_address_and_port(instance, "api.together.xyz", 443)
        request_model = kwargs.get("model", "dall-e-2")

        span_name = f"{SemanticConvetion.GEN_AI_OPERATION_TYPE_IMAGE} {request_model}"

        with tracer.start_as_current_span(span_name, kind= SpanKind.CLIENT) as span:
            start_time = time.time()
            response = wrapped(*args, **kwargs)
            end_time = time.time()

            images_count = 0

            try:
                # Find Image format
                if "response_format" in kwargs and kwargs["response_format"] == "b64_json":
                    image = "b64_json"
                else:
                    image = "url"

                image_size = str(kwargs.get('width')) + 'x' + str(kwargs.get('height'))

                # Calculate cost of the operation
                cost = get_image_model_cost(request_model,
                                            pricing_info, image_size,
                                            kwargs.get("quality", "standard"))

                for items in response.data:
                    # Set Span attributes (OTel Semconv)
                    span.set_attribute(TELEMETRY_SDK_NAME, "openlit")
                    span.set_attribute(SemanticConvetion.GEN_AI_OPERATION,
                                        SemanticConvetion.GEN_AI_OPERATION_TYPE_IMAGE)
                    span.set_attribute(SemanticConvetion.GEN_AI_SYSTEM,
                                        SemanticConvetion.GEN_AI_SYSTEM_TOGETHER)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_MODEL,
                                        request_model)
                    span.set_attribute(SemanticConvetion.SERVER_ADDRESS,
                                        server_address)
                    span.set_attribute(SemanticConvetion.SERVER_PORT,
                                        server_port)
                    span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_ID,
                                        response.id)
                    span.set_attribute(SemanticConvetion.GEN_AI_RESPONSE_MODEL,
                                        response.model)
                    span.set_attribute(SemanticConvetion.GEN_AI_OUTPUT_TYPE,
                                        "image")

                    # Set Span attributes (Extras)
                    span.set_attribute(DEPLOYMENT_ENVIRONMENT,
                                        environment)
                    span.set_attribute(SERVICE_NAME,
                                        application_name)
                    span.set_attribute(SemanticConvetion.GEN_AI_REQUEST_IMAGE_SIZE,
                                        image_size)
                    span.set_attribute(SemanticConvetion.GEN_AI_SDK_VERSION,
                                        version)

                    if trace_content:
                        span.add_event(
                            name=SemanticConvetion.GEN_AI_CONTENT_PROMPT_EVENT,
                            attributes={
                                SemanticConvetion.GEN_AI_CONTENT_PROMPT: kwargs.get("prompt", ""),
                            },
                        )
                        attribute_name = f"{SemanticConvetion.GEN_AI_RESPONSE_IMAGE}.{images_count}"
                        span.add_event(
                            name=attribute_name,
                            attributes={
                                SemanticConvetion.GEN_AI_CONTENT_COMPLETION: getattr(items, image),
                            },
                        )

                    images_count+=1

                span.set_attribute(SemanticConvetion.GEN_AI_USAGE_COST,
                                    len(response.data) * cost)
                span.set_status(Status(StatusCode.OK))

                if disable_metrics is False:
                    attributes = create_metrics_attributes(
                        service_name=application_name,
                        deployment_environment=environment,
                        operation=SemanticConvetion.GEN_AI_OPERATION_TYPE_IMAGE,
                        system=SemanticConvetion.GEN_AI_SYSTEM_TOGETHER,
                        request_model=request_model,
                        server_address=server_address,
                        server_port=server_port,
                        response_model=response.model,
                    )

                    metrics["genai_client_operation_duration"].record(
                        end_time - start_time, attributes
                    )
                    metrics["genai_requests"].add(1, attributes)
                    metrics["genai_cost"].record(cost, attributes)

                # Return original response
                return response

            except Exception as e:
                handle_exception(span, e)
                logger.error("Error in trace creation: %s", e)

                # Return original response
                return response

    return wrapper
