import random
import string
import time
from collections import OrderedDict
from typing import Dict
from unittest import mock

import pytest
import torch

from megatron.core.inference.common_inference_params import CommonInferenceParams
from megatron.core.inference.inference_request import InferenceRequest, Status
from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
    GPTInferenceWrapper,
)
from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
    InferenceWrapperConfig,
)
from megatron.core.inference.text_generation_controllers.simple_text_generation_controller import (
    SimpleTextGenerationController,
)
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
from megatron.core.models.gpt.gpt_model import GPTModel
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.transformer_config import TransformerConfig
from tests.unit_tests.test_utilities import Utils


class TestTextGenerationController:

    def setup_method(self, method):
        Utils.initialize_model_parallel(
            tensor_model_parallel_size=2, pipeline_model_parallel_size=2
        )
        model_parallel_cuda_manual_seed(123)
        self.batch_size = 4
        self.hidden_size = 12
        self.vocab_size = 100
        self.sequence_length = 64
        transformer_config = TransformerConfig(
            num_layers=4,
            hidden_size=self.hidden_size,
            num_attention_heads=4,
            use_cpu_initialization=True,
        )

        gpt_model = GPTModel(
            config=transformer_config,
            transformer_layer_spec=get_gpt_layer_local_spec(),
            vocab_size=self.vocab_size,
            max_sequence_length=self.sequence_length,
            parallel_output=True,
        ).cuda()

        inference_wrapper_config = InferenceWrapperConfig(
            hidden_size=self.hidden_size,
            inference_batch_times_seqlen_threshold=20,
            fp32_residual_connection=False,
            params_dtype=torch.float,
            padded_vocab_size=self.vocab_size,
        )

        inference_wrapped_model = GPTInferenceWrapper(gpt_model, inference_wrapper_config)

        self.mock_tokenizer = mock.Mock()

        self.text_generation_controller = SimpleTextGenerationController(
            inference_wrapped_model=inference_wrapped_model, tokenizer=self.mock_tokenizer
        )

    def teardown_method(self, method):
        Utils.destroy_model_parallel()

    def test_sample_from_logits(self):
        with pytest.raises(AssertionError) as aerror:
            self.text_generation_controller.sample_from_logits(
                last_token_logits=None,
                common_inference_params=CommonInferenceParams(top_k=2, top_p=0.4),
                vocab_size=self.vocab_size,
            )
        assert str(aerror.value) == 'Cannot have top-p and top-k both greater than zero'

        with pytest.raises(AssertionError) as aerror:
            self.text_generation_controller.sample_from_logits(
                last_token_logits=None,
                common_inference_params=CommonInferenceParams(top_p=1.4, top_k=0),
                vocab_size=self.vocab_size,
            )
        assert str(aerror.value) == 'top-p should be in (0,1]'

        with pytest.raises(AssertionError) as aerror:
            self.text_generation_controller.sample_from_logits(
                last_token_logits=torch.randn(self.batch_size, 1),
                common_inference_params=CommonInferenceParams(top_k=self.vocab_size + 10),
                vocab_size=self.vocab_size,
            )
        assert str(aerror.value) == 'top-k is larger than logit size.'

        last_token_logits = (
            torch.arange(0, self.vocab_size).repeat(self.batch_size, 1).float().cuda()
        )
        sampled_logits = self.text_generation_controller.sample_from_logits(
            last_token_logits, CommonInferenceParams(top_k=1), self.vocab_size
        )
        assert torch.all(
            sampled_logits.cpu() == torch.ones(self.batch_size) * self.vocab_size - 1
        ), f"The sampled logits should all be {self.vocab_size} but its {sampled_logits}"

        sampled_logits = self.text_generation_controller.sample_from_logits(
            last_token_logits, CommonInferenceParams(top_k=2), self.vocab_size
        )
        assert torch.all(
            sampled_logits >= self.vocab_size - 2
        ), f"The sampled logits should all be greater than {self.vocab_size-2} but its {sampled_logits}"

        l = last_token_logits[0]
        top_p = 0.3
        expected_min_value = l[l.softmax(dim=-1).cumsum(dim=-1) > top_p][0].item()
        sampled_logits = self.text_generation_controller.sample_from_logits(
            last_token_logits, CommonInferenceParams(top_p=top_p, top_k=0), self.vocab_size
        )
        assert torch.all(
            sampled_logits >= expected_min_value
        ), f"The sampled logits should all be greater than {expected_min_value} but its {sampled_logits}"

        top_p = 0.95
        temperature = 2
        expected_min_value = l[l.div_(temperature).softmax(dim=-1).cumsum(dim=-1) > top_p][0].item()
        sampled_logits = self.text_generation_controller.sample_from_logits(
            last_token_logits,
            CommonInferenceParams(top_p=top_p, temperature=temperature, top_k=0),
            self.vocab_size,
        )
        assert torch.all(
            sampled_logits >= expected_min_value
        ), f"The sampled logits should all be greater than {expected_min_value} but its {sampled_logits}"

    def test_generate_all_output_tokens_static_batch(self):
        self.mock_tokenizer.vocab_size = self.vocab_size
        self.mock_tokenizer.eod = self.vocab_size - 1
        self.mock_tokenizer.detokenize.return_value = ''.join(
            random.choices(string.ascii_letters, k=random.randint(4, 10))
        )

        active_requests: Dict[int, InferenceRequest] = OrderedDict()
        for i in range(self.batch_size):
            prompt = "sample" * (i + 1)
            self.mock_tokenizer.tokenize.return_value = torch.randn(
                self.batch_size, self.vocab_size
            ).cuda()
            inference_request = InferenceRequest(
                request_id=i,
                prompt=prompt,
                inference_parameters=CommonInferenceParams(num_tokens_to_generate=10),
                arrival_time=time.time(),
                prompt_tokens=torch.randint(
                    low=0, high=self.vocab_size - 1, size=(len(prompt),)
                ).tolist(),
                status=Status.ACTIVE_BUT_NOT_GENERATING_TOKENS,
            )
            active_requests[i] = inference_request

        requests = self.text_generation_controller.generate_all_output_tokens_static_batch(
            active_requests
        )

        for request_id, request in requests.items():
            assert (
                request.status == Status.COMPLETED
            ), f"Status should be completed but its {request.status}"
            assert request.generated_length > 0, f"Generated length should be greater than zero"
            assert request.generated_text is not None, "Generated text should not be None"
