


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


_snake_case	:						Dict         =							"sshleifer/mar_enro_6_3_student"

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Dict							)      ->       Any:
														super().setUp()

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

							@slow
							@require_torch_gpu
							def        __snake_case   (				self			:    str							)      ->       Optional[Any]:
														MarianMTModel.from_pretrained(lowerCamelCase							)






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

														# bash_script = bash_script.replace("--fp16 ", "")
														__snake_case						:     int									=  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

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

														# Check metrics
														__snake_case						:     Union[str, Any]									=  load_json(model.metrics_save_path							)
														__snake_case						:     Any									=  metrics["val"][0]
														__snake_case						:     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
														__snake_case						:     Tuple									=  os.listdir(lowerCamelCase							)
														__snake_case						:     Optional[int]									=  [x for x in contents if x.endswith(".ckpt"							)][0]
														__snake_case						:     Dict									=  os.path.join(args.output_dir       ,      lowerCamelCase							)
														__snake_case						:     str									=  torch.load(lowerCamelCase       ,      map_location="cpu"							)
														__snake_case						:     Union[str, Any]									=  "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:
																					__snake_case						:     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 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							@timeout_decorator.timeout(600							)
							@slow
							@require_torch_gpu
							def        __snake_case   (				self			:    Tuple							)      ->       int:
														__snake_case						:     int									=  F'{self.test_file_dir_str}/test_data/wmt_en_ro'
														__snake_case						:     Optional[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
														__snake_case						:     List[Any]									=  (
														    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"							)[1].strip()
														)
														__snake_case						:     int									=  bash_script.replace("\\\n"       ,      ""							).strip().replace("\"$@\""       ,      ""							)
														__snake_case						:     List[str]									=  bash_script.replace("--fp16 "       ,      " "							)

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

																					__snake_case						:     Union[str, Any]									=  distill_main(lowerCamelCase							)

														# Check metrics
														__snake_case						:     Dict									=  load_json(model.metrics_save_path							)
														__snake_case						:     List[Any]									=  metrics["val"][0]
														__snake_case						:     List[str]									=  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
														__snake_case						:     List[str]									=  os.listdir(lowerCamelCase							)
														__snake_case						:     int									=  [x for x in contents if x.endswith(".ckpt"							)][0]
														__snake_case						:     List[str]									=  os.path.join(args.output_dir       ,      lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  torch.load(lowerCamelCase       ,      map_location="cpu"							)
														__snake_case						:     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:
																					__snake_case						:     Optional[int]									=  {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



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
