




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_   (     snake_case__	):



				'''simple docstring'''
				lowerCamelCase       =  ["""vqvae"""]





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





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict							)							->			List[Any]:
											return 50 if isinstance(self.scheduler      ,							__lowercase							) else 1000





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

											if audio_file is not None or raw_audio is not None:
																		self.mel.load_audio(__lowercase      ,							__lowercase							)
																		lowerCamelCase_			:    Any   						=      self.mel.audio_slice_to_image(__lowercase							)
																		lowerCamelCase_			:    Any   						=      np.frombuffer(input_image.tobytes()      ,							dtype="uint8"							).reshape(
																		    (input_image.height, input_image.width)							)
																		lowerCamelCase_			:    Any   						=      (input_image / 255) * 2 - 1
																		lowerCamelCase_			:    Optional[int]   						=      torch.tensor(input_image[np.newaxis, :, :]      ,							dtype=torch.float							).to(self.device							)

																		if self.vqvae is not None:
																									lowerCamelCase_			:    str   						=      self.vqvae.encode(torch.unsqueeze(__lowercase      ,							0							)							).latent_dist.sample(
																									    generator=__lowercase							)[0]
																									lowerCamelCase_			:    Any   						=      self.vqvae.config.scaling_factor * input_images

																		if start_step > 0:
																									lowerCamelCase_			:    Optional[Any]   						=      self.scheduler.add_noise(__lowercase      ,							__lowercase      ,							self.scheduler.timesteps[start_step - 1]							)

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

											for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
																		if isinstance(self.unet      ,							__lowercase							):
																									lowerCamelCase_			:    Tuple   						=      self.unet(__lowercase      ,							__lowercase      ,							__lowercase							)['''sample''']
																		else:
																									lowerCamelCase_			:    Optional[Any]   						=      self.unet(__lowercase      ,							__lowercase							)['''sample''']

																		if isinstance(self.scheduler      ,							__lowercase							):
																									lowerCamelCase_			:    Dict   						=      self.scheduler.step(
																									    model_output=__lowercase      ,							timestep=__lowercase      ,							sample=__lowercase      ,							eta=__lowercase      ,							generator=__lowercase      ,							)['''prev_sample''']
																		else:
																									lowerCamelCase_			:    Union[str, Any]   						=      self.scheduler.step(
																									    model_output=__lowercase      ,							timestep=__lowercase      ,							sample=__lowercase      ,							generator=__lowercase      ,							)['''prev_sample''']

																		if mask is not None:
																									if mask_start > 0:
																																lowerCamelCase_			:    List[Any]   						=      mask[:, step, :, :mask_start]
																									if mask_end > 0:
																																lowerCamelCase_			:    List[str]   						=      mask[:, step, :, -mask_end:]

											if self.vqvae is not None:
																		# 0.18215 was scaling factor used in training to ensure unit variance
																		lowerCamelCase_			:    Dict   						=      1 / self.vqvae.config.scaling_factor * images
																		lowerCamelCase_			:    str   						=      self.vqvae.decode(__lowercase							)['''sample''']

											lowerCamelCase_			:    str   						=      (images / 2 + 0.5).clamp(0      ,							1							)
											lowerCamelCase_			:    Any   						=      images.cpu().permute(0      ,							2      ,							3      ,							1							).numpy()
											lowerCamelCase_			:    int   						=      (images * 255).round().astype("uint8"							)
											lowerCamelCase_			:    List[Any]   						=      list(
											    (Image.fromarray(_[:, :, 0]							) for _ in images)
											    if images.shape[3] == 1
											    else (Image.fromarray(__lowercase      ,							mode="RGB"							).convert("L"							) for _ in images)							)

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

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





				@torch.no_grad()
				def    __SCREAMING_SNAKE_CASE     (      self							:   str      ,							__magic_name__							:   List[Image.Image]      ,							__magic_name__							:   int = 50							)							->			Optional[Any]:
											assert isinstance(self.scheduler      ,							__lowercase							)
											self.scheduler.set_timesteps(__lowercase							)
											lowerCamelCase_			:    Optional[Any]   						=      np.array(
											    [np.frombuffer(image.tobytes()      ,							dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
											lowerCamelCase_			:    Optional[int]   						=      (sample / 255) * 2 - 1
											lowerCamelCase_			:    List[str]   						=      torch.Tensor(__lowercase							).to(self.device							)

											for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,							(0,)							)							):
																		lowerCamelCase_			:    List[str]   						=      t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																		lowerCamelCase_			:    Union[str, Any]   						=      self.scheduler.alphas_cumprod[t]
																		lowerCamelCase_			:    Optional[int]   						=      (
																		    self.scheduler.alphas_cumprod[prev_timestep]
																		    if prev_timestep >= 0
																		    else self.scheduler.final_alpha_cumprod
																		)
																		lowerCamelCase_			:    Dict   						=      1 - alpha_prod_t
																		lowerCamelCase_			:    Optional[Any]   						=      self.unet(__lowercase      ,							__lowercase							)['''sample''']
																		lowerCamelCase_			:    int   						=      (1 - alpha_prod_t_prev) ** 0.5 * model_output
																		lowerCamelCase_			:    Union[str, Any]   						=      (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																		lowerCamelCase_			:    Tuple   						=      sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

											return sample





				@staticmethod
				def    __SCREAMING_SNAKE_CASE     (      __magic_name__							:   torch.Tensor      ,							__magic_name__							:   torch.Tensor      ,							__magic_name__							:   float							)							->			List[str]:
											lowerCamelCase_			:    Tuple   						=      acos(torch.dot(torch.flatten(__lowercase							)      ,							torch.flatten(__lowercase							)							) / torch.norm(__lowercase							) / torch.norm(__lowercase							)							)
											return sin((1 - alpha) * theta							) * xa / sin(__lowercase							) + sin(alpha * theta							) * xa / sin(__lowercase							)








"""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


_UpperCamelCase					      =   'sshleifer/mar_enro_6_3_student'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




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

											__lowerCamelCase   :   int       =cached_path(
											    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''    ,     extract_compressed_file=__lowercase    ,     )
											__lowerCamelCase   :   Dict       =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:int      ):
											MarianMTModel.from_pretrained(__lowercase      )




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Tuple       ={
											    '''$MAX_LEN''': 64,
											    '''$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   :   str       =(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''      )[1].strip()
											__lowerCamelCase   :   Union[str, Any]       =bash_script.replace('''\\\n'''    ,     ''''''      ).strip().replace('''"$@"'''    ,     ''''''      )
											for k, v in env_vars_to_replace.items():
																		__lowerCamelCase   :   Tuple       =bash_script.replace(__lowercase    ,     str(__lowercase      )      )
											__lowerCamelCase   :   Union[str, Any]       =self.get_auto_remove_tmp_dir()

											# bash_script = bash_script.replace("--fp16 ", "")
											__lowerCamelCase   :   List[Any]       =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   :   str       =['''finetune.py'''] + bash_script.split() + args
											with patch.object(__lowercase    ,     '''argv'''    ,     __lowercase      ):
																		__lowerCamelCase   :   str       =argparse.ArgumentParser()
																		__lowerCamelCase   :   Dict       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   List[Any]       =SummarizationModule.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   int       =parser.parse_args()
																		__lowerCamelCase   :   Tuple       =main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Union[str, Any]       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   Any       =metrics['''val'''][0]
											__lowerCamelCase   :   int       =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}']    ,     __lowercase      )

											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   :   Any       =os.listdir(__lowercase      )
											__lowerCamelCase   :   Tuple       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   int       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   List[str]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   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   :   str       ={os.path.basename(__lowercase      ) 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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@timeout_decorator.timeout(600      )
				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   int       =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
											__lowerCamelCase   :   List[Any]       ={
											    '''--fp16_opt_level=O1''': '''''',
											    '''$MAX_LEN''': 128,
											    '''$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   :   Any       =(
											    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''      )[1].strip()
											)
											__lowerCamelCase   :   Optional[Any]       =bash_script.replace('''\\\n'''    ,     ''''''      ).strip().replace('''"$@"'''    ,     ''''''      )
											__lowerCamelCase   :   Union[str, Any]       =bash_script.replace('''--fp16 '''    ,     ''' '''      )

											for k, v in env_vars_to_replace.items():
																		__lowerCamelCase   :   str       =bash_script.replace(__lowercase    ,     str(__lowercase      )      )
											__lowerCamelCase   :   Any       =self.get_auto_remove_tmp_dir()
											__lowerCamelCase   :   Optional[int]       =bash_script.replace('''--fp16'''    ,     ''''''      )
											__lowerCamelCase   :   Tuple       =6
											__lowerCamelCase   :   str       =(
											    ['''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(__lowercase    ,     '''argv'''    ,     __lowercase      ):
																		__lowerCamelCase   :   int       =argparse.ArgumentParser()
																		__lowerCamelCase   :   int       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   Optional[int]       =SummarizationDistiller.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   Union[str, Any]       =parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		__lowerCamelCase   :   Union[str, Any]       =distill_main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Dict       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   List[str]       =metrics['''val'''][0]
											__lowerCamelCase   :   Tuple       =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}']    ,     __lowercase      )

											# check lightning ckpt can be loaded and has a reasonable statedict
											__lowerCamelCase   :   int       =os.listdir(__lowercase      )
											__lowerCamelCase   :   str       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   Union[str, Any]       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   int       ='''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   :   int       ={os.path.basename(__lowercase      ) 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




