import functools
import pytest

from devtools_testutils import AzureRecordedTestCase, EnvironmentVariableLoader, recorded_by_proxy
from azure.ai.language.conversations import ConversationAnalysisClient, AnalyzeConversationLROPoller
from azure.core.paging import ItemPaged
from azure.ai.language.conversations.models import (
    # request models
    AnalyzeConversationOperationInput,
    MultiLanguageConversationInput,
    TextConversation,
    TextConversationItem,
    PiiOperationAction,
    ConversationPiiActionContent,
    ConversationActions,
    AnalyzeConversationOperationResult,
    ConversationPiiOperationResult,
    ConversationalPiiResult,
    ConversationPiiItemResult,
    NamedEntity,
    InputWarning,
    ConversationError,
    AnalyzeConversationOperationAction,
    CharacterMaskPolicyType,
    RedactionCharacter,
)
from typing import cast, List

from azure.core.credentials import AzureKeyCredential

ConversationsPreparer = functools.partial(
    EnvironmentVariableLoader,
    "conversations",
    conversations_endpoint="https://Sanitized.cognitiveservices.azure.com/",
    conversations_key="fake_key",
)


class TestConversations(AzureRecordedTestCase):

    # Start with any helper functions you might need, for example a client creation method:
    def create_client(self, endpoint, key):
        credential = AzureKeyCredential(key)
        client = ConversationAnalysisClient(endpoint, credential)
        return client

    ...


class TestConversationsCase(TestConversations):
    @ConversationsPreparer()
    @recorded_by_proxy
    def test_conversation_pii_with_character_mask_policy(self, conversations_endpoint, conversations_key):
        client = self.create_client(conversations_endpoint, conversations_key)

        # Track redacted texts we verify
        redacted_verified: List[str] = []

        # ---- Redaction policy: mask with '*' ---------------------------------
        redaction_policy = CharacterMaskPolicyType(redaction_character=RedactionCharacter.ASTERISK)

        # ---- Build input -----------------------------------------------------
        ml_input = MultiLanguageConversationInput(
            conversations=[
                TextConversation(
                    id="1",
                    language="en",
                    conversation_items=[
                        TextConversationItem(id="1", participant_id="Agent_1", text="Can you provide your name?"),
                        TextConversationItem(id="2", participant_id="Customer_1", text="Hi, my name is John Doe."),
                        TextConversationItem(
                            id="3",
                            participant_id="Agent_1",
                            text="Thank you John, that has been updated in our system.",
                        ),
                    ],
                )
            ]
        )

        # Action with CharacterMaskPolicyType
        pii_action: AnalyzeConversationOperationAction = PiiOperationAction(
            action_content=ConversationPiiActionContent(redaction_policy=redaction_policy),
            name="Conversation PII with Character Mask Policy",
        )
        actions: List[AnalyzeConversationOperationAction] = [pii_action]

        operation_input = AnalyzeConversationOperationInput(
            conversation_input=ml_input,
            actions=actions,
        )

        # ---- Begin LRO -------------------------------------------------------
        poller: AnalyzeConversationLROPoller[ItemPaged[ConversationActions]] = client.begin_analyze_conversation_job(
            body=operation_input
        )

        print(f"Operation ID: {poller.details.get('operation_id')}")

        paged_actions: ItemPaged[ConversationActions] = poller.result()

        d = poller.details
        print(f"Job ID: {d.get('job_id')}")
        print(f"Status: {d.get('status')}")

        # ---- Iterate results and verify redaction ----------------------------
        for actions_page in paged_actions:
            for action_result in actions_page.task_results or []:
                ar = cast(AnalyzeConversationOperationResult, action_result)
                if isinstance(ar, ConversationPiiOperationResult):
                    for conversation in ar.results.conversations or []:
                        conversation = cast(ConversationalPiiResult, conversation)
                        for item in conversation.conversation_items or []:
                            item = cast(ConversationPiiItemResult, item)
                            redacted_text = (getattr(item.redacted_content, "text", None) or "").strip()
                            if not redacted_text:
                                continue

                            # Only verify when there are detected entities in the original item
                            if item.entities:
                                # Ensure original PII text is NOT present and '*' is present
                                for entity in item.entities:
                                    ent_text = cast(NamedEntity, entity).text or ""
                                    assert (
                                        ent_text not in redacted_text
                                    ), f"Expected entity '{ent_text}' to be redacted but found in: {redacted_text}"
                                assert (
                                    "*" in redacted_text
                                ), f"Expected redacted text to contain '*', got: {redacted_text}"
                                redacted_verified.append(redacted_text)

        # ---- Assertions -------------------------------------------------------
        assert (d.get("status") or "").lower() in {"succeeded", "partiallysucceeded"}
        assert len(redacted_verified) > 0, "Expected at least one redacted line to be verified."
