




'''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    __lowercase						(  __magic_name__ ):







   def 		UpperCamelCase__			(	self      )				->    Dict:
     super().setUp()

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







   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    int:
     MarianMTModel.from_pretrained(UpperCamelCase      )







   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    Any:
     __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(UpperCamelCase      ,							str(UpperCamelCase      )      )
     __a			=					self.get_auto_remove_tmp_dir()

     # bash_script = bash_script.replace("--fp16 ", "")
     __a			=					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

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

     # 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}"]      ,							UpperCamelCase      )

     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(UpperCamelCase      )
     __a			=					[x for x in contents if x.endswith('.ckpt'      )][0]
     __a			=					os.path.join(args.output_dir      ,							UpperCamelCase      )
     __a			=					torch.load(UpperCamelCase      ,							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(UpperCamelCase      ) 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    __lowercase						(  __magic_name__ ):







   @timeout_decorator.timeout(600      )
   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					f"{self.test_file_dir_str}/test_data/wmt_en_ro"
     __a			=					{
         '--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
     __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(UpperCamelCase      ,							str(UpperCamelCase      )      )
     __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(UpperCamelCase      ,							'argv'      ,							UpperCamelCase      ):
       __a			=					argparse.ArgumentParser()
       __a			=					pl.Trainer.add_argparse_args(UpperCamelCase      )
       __a			=					SummarizationDistiller.add_model_specific_args(UpperCamelCase      ,							os.getcwd()      )
       __a			=					parser.parse_args()
       # assert args.gpus == gpus THIS BREAKS for multi_gpu

       __a			=					distill_main(UpperCamelCase      )

     # 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}"]      ,							UpperCamelCase      )

     # check lightning ckpt can be loaded and has a reasonable statedict
     __a			=					os.listdir(UpperCamelCase      )
     __a			=					[x for x in contents if x.endswith('.ckpt'      )][0]
     __a			=					os.path.join(args.output_dir      ,							UpperCamelCase      )
     __a			=					torch.load(UpperCamelCase      ,							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(UpperCamelCase      ) 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









'''simple docstring'''


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



