import re
from datetime import datetime as dt, timezone as tz

from celery import states
from flask import current_app
# OpenAI imports
from langchain.text_splitter import MarkdownHeaderTextSplitter
from langchain_core.exceptions import LangChainException
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from sqlalchemy import or_
from sqlalchemy.exc import SQLAlchemyError

from common.extensions import db
from common.models.document import DocumentVersion, Embedding, Document, Processor, Catalog
from common.models.user import Tenant
from common.utils.celery_utils import current_celery
from common.utils.database import Database
from common.utils.model_utils import create_language_template, get_model_variables, get_embedding_model_and_class, \
    get_embedding_llm

from common.utils.business_event import BusinessEvent
from common.utils.business_event_context import current_event
from config.type_defs.processor_types import PROCESSOR_TYPES
from eveai_workers.processors.processor_registry import ProcessorRegistry

from common.utils.eveai_exceptions import EveAIInvalidEmbeddingModel

from common.utils.config_field_types import json_to_pattern_list


# Healthcheck task
@current_celery.task(name='ping', queue='embeddings')
def ping():
    return 'pong'


@current_celery.task(name='create_embeddings', queue='embeddings')
def create_embeddings(tenant_id, document_version_id):
    document_version = None
    try:
        # Retrieve Tenant for which we are processing
        tenant = Tenant.query.get(tenant_id)
        if tenant is None:
            raise Exception(f'Tenant {tenant_id} not found')

        # Ensure we are working in the correct database schema
        Database(tenant_id).switch_schema()

        # Retrieve document version to process
        document_version = DocumentVersion.query.get(document_version_id)
        if document_version is None:
            raise Exception(f'Document version {document_version_id} not found')

        # Retrieve the Catalog ID
        doc = Document.query.get_or_404(document_version.doc_id)
        catalog_id = doc.catalog_id
        catalog = Catalog.query.get_or_404(catalog_id)

        # Select variables to work with depending on tenant and model
        model_variables = get_model_variables(tenant_id)

        # Define processor related information
        processor_type, processor_class = ProcessorRegistry.get_processor_for_file_type(document_version.file_type)
        processor = get_processor_for_document(catalog_id, document_version.file_type, document_version.sub_file_type)

    except Exception as e:
        current_app.logger.error(f'Create Embeddings request received '
                                 f'for badly configured document version {document_version_id} '
                                 f'for tenant {tenant_id}, '
                                 f'error: {e}')
        if document_version:
            document_version.processing_error = str(e)
        raise

    # BusinessEvent creates a context, which is why we need to use it with a with block
    with BusinessEvent('Create Embeddings', tenant_id,
                       document_version_id=document_version_id,
                       document_version_file_size=document_version.file_size):
        current_app.logger.info(f'Creating embeddings for tenant {tenant_id} on document version {document_version_id}')

        try:
            db.session.add(document_version)

            # start processing
            document_version.processing = True
            document_version.processing_started_at = dt.now(tz.utc)
            document_version.processing_finished_at = None
            document_version.processing_error = None

            db.session.commit()
        except SQLAlchemyError as e:
            current_app.logger.error(f'Unable to save Embedding status information '
                                     f'in document version {document_version_id} '
                                     f'for tenant {tenant_id}')
            raise

        delete_embeddings_for_document_version(document_version)

        try:
            with current_event.create_span(f"{processor_type} Processing"):
                document_processor = processor_class(
                    tenant=tenant,
                    model_variables=model_variables,
                    document_version=document_version,
                    catalog=catalog,
                    processor=processor
                )
                markdown, title = document_processor.process()
                document_processor.log_tuning("Processor returned: ", {
                    'markdown': markdown,
                    'title': title
                })

            with current_event.create_span("Embedding"):
                embed_markdown(tenant, model_variables, document_version, catalog, document_processor, markdown, title)

            current_event.log("Finished Embedding Creation Task")

        except Exception as e:
            current_app.logger.error(f'Error creating embeddings for tenant {tenant_id} '
                                     f'on document version {document_version_id} '
                                     f'error: {e}')
            document_version.processing = False
            document_version.processing_finished_at = dt.now(tz.utc)
            document_version.processing_error = str(e)[:255]
            db.session.commit()
            create_embeddings.update_state(state=states.FAILURE)
            raise


def delete_embeddings_for_document_version(document_version):
    embeddings_to_delete = db.session.query(Embedding).filter_by(doc_vers_id=document_version.id).all()
    for embedding in embeddings_to_delete:
        db.session.delete(embedding)
    try:
        db.session.commit()
        current_app.logger.info(f'Deleted embeddings for document version {document_version.id}')
    except SQLAlchemyError as e:
        current_app.logger.error(f'Unable to delete embeddings for document version {document_version.id}')
        raise


def embed_markdown(tenant, model_variables, document_version, catalog, processor, markdown, title):
    # Create potential chunks
    potential_chunks = create_potential_chunks_for_markdown(tenant.id, document_version, processor, markdown)
    processor.log_tuning("Potential Chunks: ", {'potential chunks': potential_chunks})

    # Combine chunks for embedding
    chunks = combine_chunks_for_markdown(potential_chunks, catalog.min_chunk_size, catalog.max_chunk_size, processor)
    processor.log_tuning("Chunks: ", {'chunks': chunks})

    # Enrich chunks
    with current_event.create_span("Enrich Chunks"):
        enriched_chunks = enrich_chunks(tenant, model_variables, document_version, title, chunks)
        processor.log_tuning("Enriched Chunks: ", {'enriched_chunks': enriched_chunks})

    # Create embeddings
    with current_event.create_span("Create Embeddings"):
        embeddings = embed_chunks(tenant, catalog, document_version, enriched_chunks)

    # Update document version and save embeddings
    try:
        db.session.add(document_version)
        document_version.processing_finished_at = dt.now(tz.utc)
        document_version.processing = False
        db.session.add_all(embeddings)
        db.session.commit()
    except SQLAlchemyError as e:
        current_app.logger.error(f'Error saving embedding information for tenant {tenant.id} '
                                 f'on HTML, document version {document_version.id}'
                                 f'error: {e}')
        raise

    current_app.logger.info(f'Embeddings created successfully for tenant {tenant.id} '
                            f'on document version {document_version.id} :-)')


def enrich_chunks(tenant, model_variables, document_version, title, chunks):
    summary = ''
    if len(chunks) > 1:
        summary = summarize_chunk(tenant, model_variables, document_version, chunks[0])

    chunk_total_context = (f'Filename: {document_version.object_name}\n'
                           f'User Context:\n{document_version.user_context}\n\n'
                           f'User Metadata:\n{document_version.user_metadata}\n\n'
                           f'Title: {title}\n'
                           f'Summary:\n{summary}\n'
                           f'System Context:\n{document_version.system_context}\n\n'
                           f'System Metadata:\n{document_version.system_metadata}\n\n'
                           )
    enriched_chunks = []
    initial_chunk = (f'Filename: {document_version.object_name}\n'
                     f'User Context:\n{document_version.user_context}\n\n'
                     f'User Metadata:\n{document_version.user_metadata}\n\n'
                     f'Title: {title}\n'
                     f'System Context:\n{document_version.system_context}\n\n'
                     f'System Metadata:\n{document_version.system_metadata}\n\n'
                     f'{chunks[0]}'
                     )

    enriched_chunks.append(initial_chunk)
    for chunk in chunks[1:]:
        enriched_chunk = f'{chunk_total_context}\n{chunk}'
        enriched_chunks.append(enriched_chunk)

    return enriched_chunks


def summarize_chunk(tenant, model_variables, document_version, chunk):
    current_event.log("Starting Summarizing Chunk")
    llm = get_embedding_llm()
    template = model_variables.get_template("summary")
    language_template = create_language_template(template, document_version.language)
    summary_prompt = ChatPromptTemplate.from_template(language_template)
    setup = RunnablePassthrough()
    output_parser = StrOutputParser()

    chain = setup | summary_prompt | llm | output_parser

    try:
        summary = chain.invoke({"text": chunk})
        current_event.log("Finished Summarizing Chunk")
        return summary
    except LangChainException as e:
        current_app.logger.error(f'Error creating summary for chunk enrichment for tenant {tenant.id} '
                                 f'on document version {document_version.id} '
                                 f'error: {e}')
        raise


def embed_chunks(tenant, catalog, document_version, chunks):
    embedding_model, embedding_model_class = get_embedding_model_and_class(tenant.id, catalog.id)
    # Actually embed
    try:
        embeddings = embedding_model.embed_documents(chunks)
    except LangChainException as e:
        current_app.logger.error(f'Error creating embeddings for tenant {tenant.id} '
                                 f'on document version {document_version.id} while calling OpenAI API'
                                 f'error: {e}')
        raise

    # Add embeddings to the database
    new_embeddings = []
    for chunk, embedding in zip(chunks, embeddings):
        new_embedding = embedding_model_class()
        new_embedding.document_version = document_version
        new_embedding.active = True
        new_embedding.chunk = chunk
        new_embedding.embedding = embedding
        new_embeddings.append(new_embedding)

    return new_embeddings


def create_potential_chunks_for_markdown(tenant_id, document_version, processor, markdown):
    try:
        current_app.logger.info(f'Creating potential chunks for tenant {tenant_id}')
        heading_level = processor.configuration.get('chunking_heading_level', 2)

        headers_to_split_on = [
            (f"{'#' * i}", f"Header {i}") for i in range(1, min(heading_level + 1, 7))
        ]

        processor.log_tuning('Headers to split on', {'header list: ': headers_to_split_on})

        markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on, strip_headers=False)
        md_header_splits = markdown_splitter.split_text(markdown)
        potential_chunks = [doc.page_content for doc in md_header_splits]

        return potential_chunks
    except Exception as e:
        current_app.logger.error(f'Error creating potential chunks for tenant {tenant_id}, with error: {e}')
        raise


def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars, processor):
    actual_chunks = []
    current_chunk = ""
    current_length = 0

    def matches_chunking_pattern(text, patterns):
        if not patterns:
            return False

        # Get the first line of the text
        first_line = text.split('\n', 1)[0].strip()

        # Check if it's a header at appropriate level
        header_match = re.match(r'^(#{1,6})\s+(.+)$', first_line)
        if not header_match:
            return False

        # Get the heading level (number of #s)
        header_level = len(header_match.group(1))
        # Get the header text
        header_text = header_match.group(2)

        # Check if header matches any pattern
        for pattern in patterns:
            try:
                processor.log_tuning('Pattern check: ', {
                    'pattern: ': pattern,
                    'text': header_text
                })
                if re.search(pattern, header_text, re.IGNORECASE):
                    return True
            except Exception as e:
                current_app.logger.warning(f"Invalid regex pattern '{pattern}': {str(e)}")
                continue

        return False

    chunking_patterns = json_to_pattern_list(processor.configuration.get('chunking_patterns', ""))

    processor.log_tuning(f'Chunking Patterns Extraction: ', {
        'Full Configuration': processor.configuration,
        'Chunking Patterns': chunking_patterns,
    })

    for chunk in potential_chunks:
        chunk_length = len(chunk)

        # Force new chunk if pattern matches
        if chunking_patterns and matches_chunking_pattern(chunk, chunking_patterns):
            if current_chunk and current_length >= min_chars:
                actual_chunks.append(current_chunk)
            current_chunk = chunk
            current_length = chunk_length
            continue

        if current_length + chunk_length > max_chars:
            if current_length >= min_chars:
                actual_chunks.append(current_chunk)
                current_chunk = chunk
                current_length = chunk_length
            else:
                # If the combined chunk is still less than max_chars, keep adding
                current_chunk += f'\n{chunk}'
                current_length += chunk_length
        else:
            current_chunk += f'\n{chunk}'
            current_length += chunk_length

    # Handle the last chunk
    if current_chunk and current_length >= 0:
        actual_chunks.append(current_chunk)

    return actual_chunks


def get_processor_for_document(catalog_id: int, file_type: str, sub_file_type: str = None) -> Processor:
    """
    Get the appropriate processor for a document based on catalog_id, file_type and optional sub_file_type.

    Args:
        catalog_id: ID of the catalog
        file_type: Type of file (e.g., 'pdf', 'html')
        sub_file_type: Optional sub-type for specialized processing

    Returns:
        Processor instance

    Raises:
        ValueError: If no matching processor is found
    """
    try:
        # Start with base query for catalog
        query = Processor.query.filter_by(catalog_id=catalog_id)

        # Find processor type that handles this file type
        matching_processor_type = None
        for proc_type, config in PROCESSOR_TYPES.items():
            supported_types = config['file_types']
            if isinstance(supported_types, str):
                supported_types = [t.strip() for t in supported_types.split(',')]

            if file_type in supported_types:
                matching_processor_type = proc_type
                break

        if not matching_processor_type:
            raise ValueError(f"No processor type found for file type: {file_type}")

        # Add processor type condition
        query = query.filter_by(type=matching_processor_type)

        # If sub_file_type is provided, add that condition
        if sub_file_type:
            query = query.filter_by(sub_file_type=sub_file_type)
        else:
            # If no sub_file_type, prefer processors without sub_file_type specification
            query = query.filter(or_(Processor.sub_file_type.is_(None),
                                     Processor.sub_file_type == ''))

        # Get the first matching processor
        processor = query.first()

        if not processor:
            if sub_file_type:
                raise ValueError(
                    f"No processor found for catalog {catalog_id} of type {matching_processor_type}, "
                    f"file type {file_type}, sub-type {sub_file_type}"
                )
            else:
                raise ValueError(
                    f"No processor found for catalog {catalog_id}, "
                    f"file type {file_type}"
                )

        return processor

    except Exception as e:
        current_app.logger.error(f"Error finding processor: {str(e)}")
        raise