



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


SCREAMING_SNAKE_CASE__       =      '''sshleifer/mar_enro_6_3_student'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								super().setUp()

								A__       =    cached_path(
								    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'				,					extract_compressed_file=_snake_case				,					)
								A__       =    F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''



					@slow
					@require_torch_gpu
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								MarianMTModel.from_pretrained(_snake_case					)



					@slow
					@require_torch_gpu
					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    {
								    '$MAX_LEN': 64,
								    '$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
								A__       =    (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'					)[1].strip()
								A__       =    bash_script.replace('\\\n'				,					''					).strip().replace('"$@"'				,					''					)
								for k, v in env_vars_to_replace.items():
											A__       =    bash_script.replace(_snake_case				,					str(_snake_case					)					)
								A__       =    self.get_auto_remove_tmp_dir()

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

								A__       =    ['finetune.py'] + bash_script.split() + args
								with patch.object(_snake_case				,					'argv'				,					_snake_case					):
											A__       =    argparse.ArgumentParser()
											A__       =    pl.Trainer.add_argparse_args(_snake_case					)
											A__       =    SummarizationModule.add_model_specific_args(_snake_case				,					os.getcwd()					)
											A__       =    parser.parse_args()
											A__       =    main(_snake_case					)

								# Check metrics
								A__       =    load_json(model.metrics_save_path					)
								A__       =    metrics['val'][0]
								A__       =    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}''']				,					_snake_case					)

								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
								A__       =    os.listdir(_snake_case					)
								A__       =    [x for x in contents if x.endswith('.ckpt'					)][0]
								A__       =    os.path.join(args.output_dir				,					_snake_case					)
								A__       =    torch.load(_snake_case				,					map_location='cpu'					)
								A__       =    '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:
											A__       =    {os.path.basename(_snake_case					) 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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@timeout_decorator.timeout(6_00					)
					@slow
					@require_torch_gpu
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								A__       =    {
								    '--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
								A__       =    (
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'					)[1].strip()
								)
								A__       =    bash_script.replace('\\\n'				,					''					).strip().replace('"$@"'				,					''					)
								A__       =    bash_script.replace('--fp16 '				,					' '					)

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

											A__       =    distill_main(_snake_case					)

								# Check metrics
								A__       =    load_json(model.metrics_save_path					)
								A__       =    metrics['val'][0]
								A__       =    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}''']				,					_snake_case					)

								# check lightning ckpt can be loaded and has a reasonable statedict
								A__       =    os.listdir(_snake_case					)
								A__       =    [x for x in contents if x.endswith('.ckpt'					)][0]
								A__       =    os.path.join(args.output_dir				,					_snake_case					)
								A__       =    torch.load(_snake_case				,					map_location='cpu'					)
								A__       =    '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:
											A__       =    {os.path.basename(_snake_case					) 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 random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs



					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				,					reason='XFormers attention is only available with CUDA and `xformers` installed'				,					)
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								super().test_save_load_floataa(expected_max_diff=1E-1					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,					)




