


'''simple docstring'''






from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['vqvae']

    def __init__(       self   :  Dict   ,						lowerCamelCase   :  AutoencoderKL   ,						lowerCamelCase   :  UNetaDConditionModel   ,						lowerCamelCase   :  Mel   ,						lowerCamelCase   :  Union[DDIMScheduler, DDPMScheduler]   ,						)       ->  Union[str, Any]:
      super().__init__()
      self.register_modules(unet=lowerCamelCase   ,						scheduler=lowerCamelCase   ,						mel=lowerCamelCase   ,						vqvae=lowerCamelCase		)

    def __lowercase       (       self   :  Any		)       ->  int:
      return 50 if isinstance(self.scheduler   ,						lowerCamelCase		) else 10_00

    @torch.no_grad()
    def __call__(       self   :  int   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  str = None   ,						lowerCamelCase   :  np.ndarray = None   ,						lowerCamelCase   :  int = 0   ,						lowerCamelCase   :  int = 0   ,						lowerCamelCase   :  int = None   ,						lowerCamelCase   :  torch.Generator = None   ,						lowerCamelCase   :  float = 0   ,						lowerCamelCase   :  float = 0   ,						lowerCamelCase   :  torch.Generator = None   ,						lowerCamelCase   :  float = 0   ,						lowerCamelCase   :  torch.Tensor = None   ,						lowerCamelCase   :  torch.Tensor = None   ,						lowerCamelCase   :  Optional[Any]=True   ,						)       ->  Union[
        Union[AudioPipelineOutput, ImagePipelineOutput],
        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
    ]:
      lowerCAmelCase_      :				List[str]      =       steps or self.get_default_steps()
      self.scheduler.set_timesteps(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size		) == int:
        lowerCAmelCase_      :				List[Any]      =       (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
        lowerCAmelCase_      :				List[str]      =       randn_tensor(
            (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size[0],
                self.unet.config.sample_size[1],
            )   ,						generator=lowerCamelCase   ,						device=self.device   ,						)
      lowerCAmelCase_      :				Optional[int]      =       noise
      lowerCAmelCase_      :				int      =       None

      if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(lowerCamelCase   ,						lowerCamelCase		)
        lowerCAmelCase_      :				int      =       self.mel.audio_slice_to_image(lowerCamelCase		)
        lowerCAmelCase_      :				List[Any]      =       np.frombuffer(input_image.tobytes()   ,						dtype="""uint8"""		).reshape(
            (input_image.height, input_image.width)		)
        lowerCAmelCase_      :				List[str]      =       (input_image / 2_55) * 2 - 1
        lowerCAmelCase_      :				Tuple      =       torch.tensor(input_image[np.newaxis, :, :]   ,						dtype=torch.float		).to(self.device		)

        if self.vqvae is not None:
          lowerCAmelCase_      :				Optional[Any]      =       self.vqvae.encode(torch.unsqueeze(lowerCamelCase   ,						0		)		).latent_dist.sample(
              generator=lowerCamelCase		)[0]
          lowerCAmelCase_      :				Tuple      =       self.vqvae.config.scaling_factor * input_images

        if start_step > 0:
          lowerCAmelCase_      :				str      =       self.scheduler.add_noise(lowerCamelCase   ,						lowerCamelCase   ,						self.scheduler.timesteps[start_step - 1]		)

        lowerCAmelCase_      :				Optional[Any]      =       (
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        lowerCAmelCase_      :				Optional[int]      =       int(mask_start_secs * pixels_per_second		)
        lowerCAmelCase_      :				Optional[Any]      =       int(mask_end_secs * pixels_per_second		)
        lowerCAmelCase_      :				Tuple      =       self.scheduler.add_noise(lowerCamelCase   ,						lowerCamelCase   ,						torch.tensor(self.scheduler.timesteps[start_step:]		)		)

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
        if isinstance(self.unet   ,						lowerCamelCase		):
          lowerCAmelCase_      :				List[str]      =       self.unet(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)["""sample"""]
        else:
          lowerCAmelCase_      :				Dict      =       self.unet(lowerCamelCase   ,						lowerCamelCase		)["""sample"""]

        if isinstance(self.scheduler   ,						lowerCamelCase		):
          lowerCAmelCase_      :				List[Any]      =       self.scheduler.step(
              model_output=lowerCamelCase   ,						timestep=lowerCamelCase   ,						sample=lowerCamelCase   ,						eta=lowerCamelCase   ,						generator=lowerCamelCase   ,						)["""prev_sample"""]
        else:
          lowerCAmelCase_      :				List[str]      =       self.scheduler.step(
              model_output=lowerCamelCase   ,						timestep=lowerCamelCase   ,						sample=lowerCamelCase   ,						generator=lowerCamelCase   ,						)["""prev_sample"""]

        if mask is not None:
          if mask_start > 0:
            lowerCAmelCase_      :				List[str]      =       mask[:, step, :, :mask_start]
          if mask_end > 0:
            lowerCAmelCase_      :				Optional[int]      =       mask[:, step, :, -mask_end:]

      if self.vqvae is not None:
        # 0.18215 was scaling factor used in training to ensure unit variance
        lowerCAmelCase_      :				List[str]      =       1 / self.vqvae.config.scaling_factor * images
        lowerCAmelCase_      :				Optional[int]      =       self.vqvae.decode(lowerCamelCase		)["""sample"""]

      lowerCAmelCase_      :				Optional[int]      =       (images / 2 + 0.5).clamp(0   ,						1		)
      lowerCAmelCase_      :				Any      =       images.cpu().permute(0   ,						2   ,						3   ,						1		).numpy()
      lowerCAmelCase_      :				Optional[Any]      =       (images * 2_55).round().astype("""uint8"""		)
      lowerCAmelCase_      :				Optional[Any]      =       list(
          (Image.fromarray(_[:, :, 0]		) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(lowerCamelCase   ,						mode="""RGB"""		).convert("""L"""		) for _ in images)		)

      lowerCAmelCase_      :				List[str]      =       [self.mel.image_to_audio(lowerCamelCase		) for _ in images]
      if not return_dict:
        return images, (self.mel.get_sample_rate(), audios)

      return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase		)[:, np.newaxis, :]		)   ,						**ImagePipelineOutput(lowerCamelCase		)		)

    @torch.no_grad()
    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  List[Image.Image]   ,						lowerCamelCase   :  int = 50		)       ->  np.ndarray:
      assert isinstance(self.scheduler   ,						lowerCamelCase		)
      self.scheduler.set_timesteps(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       np.array(
          [np.frombuffer(image.tobytes()   ,						dtype="""uint8"""		).reshape((1, image.height, image.width)		) for image in images]		)
      lowerCAmelCase_      :				List[Any]      =       (sample / 2_55) * 2 - 1
      lowerCAmelCase_      :				Any      =       torch.Tensor(lowerCamelCase		).to(self.device		)

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,						(0,)		)		):
        lowerCAmelCase_      :				Optional[Any]      =       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
        lowerCAmelCase_      :				Optional[Any]      =       self.scheduler.alphas_cumprod[t]
        lowerCAmelCase_      :				Union[str, Any]      =       (
            self.scheduler.alphas_cumprod[prev_timestep]
            if prev_timestep >= 0
            else self.scheduler.final_alpha_cumprod
        )
        lowerCAmelCase_      :				int      =       1 - alpha_prod_t
        lowerCAmelCase_      :				List[str]      =       self.unet(lowerCamelCase   ,						lowerCamelCase		)["""sample"""]
        lowerCAmelCase_      :				List[str]      =       (1 - alpha_prod_t_prev) ** 0.5 * model_output
        lowerCAmelCase_      :				Dict      =       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
        lowerCAmelCase_      :				Optional[int]      =       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample





    @staticmethod
    def __lowercase       (       lowerCamelCase   :  torch.Tensor   ,						lowerCamelCase   :  torch.Tensor   ,						lowerCamelCase   :  float		)       ->  torch.Tensor:
      lowerCAmelCase_      :				Union[str, Any]      =       acos(torch.dot(torch.flatten(lowerCamelCase		)   ,						torch.flatten(lowerCamelCase		)		) / torch.norm(lowerCamelCase		) / torch.norm(lowerCamelCase		)		)
      return sin((1 - alpha) * theta		) * xa / sin(lowerCamelCase		) + sin(alpha * theta		) * xa / sin(lowerCamelCase		)







'''simple docstring'''






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

      lowerCAmelCase_      :				Any      =       cached_path(
          """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,						extract_compressed_file=lowerCamelCase   ,						)
      lowerCAmelCase_      :				Optional[Any]      =       F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$BS""": 64,
          """$GAS""": 1,
          """$ENRO_DIR""": self.data_dir,
          """facebook/mbart-large-cc25""": MARIAN_MODEL,
          # "val_check_interval=0.25": "val_check_interval=1.0",
          """--learning_rate=3e-5""": """--learning_rate 3e-4""",
          """--num_train_epochs 6""": """--num_train_epochs 1""",
      }

      # Clean up bash script
      lowerCAmelCase_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       F'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
      # XXX: args.gpus > 1 : handle multi_gpu in the future

      lowerCAmelCase_      :				Tuple      =       ["""finetune.py"""] + bash_script.split() + args
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				Any      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationModule.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				Tuple      =       parser.parse_args()
        lowerCAmelCase_      :				Dict      =       main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       metrics["""val"""][-1]
      self.assertEqual(len(metrics["""val"""]		)   ,						(args.max_epochs / args.val_check_interval)		)
      assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

      self.assertGreater(last_step_stats["""val_avg_gen_time"""]   ,						0.01		)
      # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
      self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]   ,						1.0		)

      # test learning requirements:

      # 1. BLEU improves over the course of training by more than 2 pts
      self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]   ,						2		)

      # 2. BLEU finishes above 17
      self.assertGreater(last_step_stats["""val_avg_bleu"""]   ,						17		)

      # 3. test BLEU and val BLEU within ~1.1 pt.
      self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]		)   ,						1.1		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				List[str]      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
      assert expected_key in ckpt["state_dict"]
      assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

      # TODO: turn on args.do_predict when PL bug fixed.
      if args.do_predict:
        lowerCAmelCase_      :				List[str]      =       {os.path.basename(lowerCamelCase		) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["""test"""]		) == 1







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--fp16_opt_level=O1""": """""",
          """$MAX_LEN""": 1_28,
          """$BS""": 16,
          """$GAS""": 1,
          """$ENRO_DIR""": data_dir,
          """$m""": """sshleifer/student_marian_en_ro_6_1""",
          """val_check_interval=0.25""": """val_check_interval=1.0""",
      }

      # Clean up bash script
      lowerCAmelCase_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				int      =       self.get_auto_remove_tmp_dir()
      lowerCAmelCase_      :				Optional[Any]      =       bash_script.replace("""--fp16"""   ,						""""""		)
      lowerCAmelCase_      :				Dict      =       6
      lowerCAmelCase_      :				List[Any]      =       (
          ["""distillation.py"""]
          + bash_script.split()
          + [
              F'--output_dir={output_dir}',
              """--gpus=1""",
              """--learning_rate=1e-3""",
              F'--num_train_epochs={epochs}',
              """--warmup_steps=10""",
              """--val_check_interval=1.0""",
              """--do_predict""",
          ]
      )
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				int      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationDistiller.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][-1]
      assert len(metrics["""val"""]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

      assert last_step_stats["val_avg_gen_time"] >= 0.01

      assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
      assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
      assert isinstance(last_step_stats[F'val_avg_{model.val_metric}']   ,						lowerCamelCase		)

      # check lightning ckpt can be loaded and has a reasonable statedict
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				Tuple      =       """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
      assert expected_key in ckpt["state_dict"]
      assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

      # TODO: turn on args.do_predict when PL bug fixed.
      if args.do_predict:
        lowerCAmelCase_      :				Union[str, Any]      =       {os.path.basename(lowerCamelCase		) for p in contents}
        assert "test_generations.txt" in contents
        assert "test_results.txt" in contents
        # assert len(metrics["val"]) ==  desired_n_evals
        assert len(metrics["""test"""]		) == 1



