# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os
import sys
import tempfile

import safetensors


sys.path.append("..")
from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402


logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger()
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class DreamBoothLoRAFluxAdvanced(ExamplesTestsAccelerate):
    instance_data_dir = "docs/source/en/imgs"
    instance_prompt = "photo"
    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-pipe"
    script_path = "examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py"

    def test_dreambooth_lora_flux(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                {self.script_path}
                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
                --instance_data_dir {self.instance_data_dir}
                --instance_prompt {self.instance_prompt}
                --resolution 64
                --train_batch_size 1
                --gradient_accumulation_steps 1
                --max_train_steps 2
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                """.split()

            run_command(self._launch_args + test_args)
            # save_pretrained smoke test
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))

            # make sure the state_dict has the correct naming in the parameters.
            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
            is_lora = all("lora" in k for k in lora_state_dict.keys())
            self.assertTrue(is_lora)

            # when not training the text encoder, all the parameters in the state dict should start
            # with `"transformer"` in their names.
            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
            self.assertTrue(starts_with_transformer)

    def test_dreambooth_lora_text_encoder_flux(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                {self.script_path}
                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
                --instance_data_dir {self.instance_data_dir}
                --instance_prompt {self.instance_prompt}
                --resolution 64
                --train_batch_size 1
                --train_text_encoder
                --gradient_accumulation_steps 1
                --max_train_steps 2
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                """.split()

            run_command(self._launch_args + test_args)
            # save_pretrained smoke test
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))

            # make sure the state_dict has the correct naming in the parameters.
            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
            is_lora = all("lora" in k for k in lora_state_dict.keys())
            self.assertTrue(is_lora)

            starts_with_expected_prefix = all(
                (key.startswith("transformer") or key.startswith("text_encoder")) for key in lora_state_dict.keys()
            )
            self.assertTrue(starts_with_expected_prefix)

    def test_dreambooth_lora_pivotal_tuning_flux_clip(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                {self.script_path}
                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
                --instance_data_dir {self.instance_data_dir}
                --instance_prompt {self.instance_prompt}
                --resolution 64
                --train_batch_size 1
                --train_text_encoder_ti
                --gradient_accumulation_steps 1
                --max_train_steps 2
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                """.split()

            run_command(self._launch_args + test_args)
            # save_pretrained smoke test
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
            # make sure embeddings were also saved
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")))

            # make sure the state_dict has the correct naming in the parameters.
            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
            is_lora = all("lora" in k for k in lora_state_dict.keys())
            self.assertTrue(is_lora)

            # make sure the state_dict has the correct naming in the parameters.
            textual_inversion_state_dict = safetensors.torch.load_file(
                os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")
            )
            is_clip = all("clip_l" in k for k in textual_inversion_state_dict.keys())
            self.assertTrue(is_clip)

            # when performing pivotal tuning, all the parameters in the state dict should start
            # with `"transformer"` in their names.
            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
            self.assertTrue(starts_with_transformer)

    def test_dreambooth_lora_pivotal_tuning_flux_clip_t5(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                {self.script_path}
                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
                --instance_data_dir {self.instance_data_dir}
                --instance_prompt {self.instance_prompt}
                --resolution 64
                --train_batch_size 1
                --train_text_encoder_ti
                --enable_t5_ti
                --gradient_accumulation_steps 1
                --max_train_steps 2
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                """.split()

            run_command(self._launch_args + test_args)
            # save_pretrained smoke test
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
            # make sure embeddings were also saved
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")))

            # make sure the state_dict has the correct naming in the parameters.
            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
            is_lora = all("lora" in k for k in lora_state_dict.keys())
            self.assertTrue(is_lora)

            # make sure the state_dict has the correct naming in the parameters.
            textual_inversion_state_dict = safetensors.torch.load_file(
                os.path.join(tmpdir, f"{os.path.basename(tmpdir)}_emb.safetensors")
            )
            is_te = all(("clip_l" in k or "t5" in k) for k in textual_inversion_state_dict.keys())
            self.assertTrue(is_te)

            # when performing pivotal tuning, all the parameters in the state dict should start
            # with `"transformer"` in their names.
            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
            self.assertTrue(starts_with_transformer)

    def test_dreambooth_lora_latent_caching(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
                {self.script_path}
                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
                --instance_data_dir {self.instance_data_dir}
                --instance_prompt {self.instance_prompt}
                --resolution 64
                --train_batch_size 1
                --gradient_accumulation_steps 1
                --max_train_steps 2
                --cache_latents
                --learning_rate 5.0e-04
                --scale_lr
                --lr_scheduler constant
                --lr_warmup_steps 0
                --output_dir {tmpdir}
                """.split()

            run_command(self._launch_args + test_args)
            # save_pretrained smoke test
            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))

            # make sure the state_dict has the correct naming in the parameters.
            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
            is_lora = all("lora" in k for k in lora_state_dict.keys())
            self.assertTrue(is_lora)

            # when not training the text encoder, all the parameters in the state dict should start
            # with `"transformer"` in their names.
            starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
            self.assertTrue(starts_with_transformer)

    def test_dreambooth_lora_flux_checkpointing_checkpoints_total_limit(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
            {self.script_path}
            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
            --instance_data_dir={self.instance_data_dir}
            --output_dir={tmpdir}
            --instance_prompt={self.instance_prompt}
            --resolution=64
            --train_batch_size=1
            --gradient_accumulation_steps=1
            --max_train_steps=6
            --checkpoints_total_limit=2
            --checkpointing_steps=2
            """.split()

            run_command(self._launch_args + test_args)

            self.assertEqual(
                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
                {"checkpoint-4", "checkpoint-6"},
            )

    def test_dreambooth_lora_flux_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            test_args = f"""
            {self.script_path}
            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
            --instance_data_dir={self.instance_data_dir}
            --output_dir={tmpdir}
            --instance_prompt={self.instance_prompt}
            --resolution=64
            --train_batch_size=1
            --gradient_accumulation_steps=1
            --max_train_steps=4
            --checkpointing_steps=2
            """.split()

            run_command(self._launch_args + test_args)

            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-2", "checkpoint-4"})

            resume_run_args = f"""
            {self.script_path}
            --pretrained_model_name_or_path={self.pretrained_model_name_or_path}
            --instance_data_dir={self.instance_data_dir}
            --output_dir={tmpdir}
            --instance_prompt={self.instance_prompt}
            --resolution=64
            --train_batch_size=1
            --gradient_accumulation_steps=1
            --max_train_steps=8
            --checkpointing_steps=2
            --resume_from_checkpoint=checkpoint-4
            --checkpoints_total_limit=2
            """.split()

            run_command(self._launch_args + resume_run_args)

            self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-6", "checkpoint-8"})
