






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       __SCREAMING_SNAKE_CASE   :






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'				)

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

												torch.manual_seed(0				)
												lowerCAmelCase__        =  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				)
												lowerCAmelCase__        =  DDPMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='squaredcos_cap_v2'       ,  beta_start=0.00_01       ,  beta_end=0.02       ,  thresholding=A__       ,  dynamic_thresholding_ratio=0.95       ,  sample_max_value=1.0       ,  prediction_type='epsilon'       ,  variance_type='learned_range'       ,  )

												torch.manual_seed(0				)
												lowerCAmelCase__        =  IFWatermarker()

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'				)

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

												torch.manual_seed(0				)
												lowerCAmelCase__        =  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=1.4_14       ,  time_embedding_act_fn='gelu'       ,  time_embedding_dim=32       ,  )
												unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

												torch.manual_seed(0				)
												lowerCAmelCase__        =  DDPMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='squaredcos_cap_v2'       ,  beta_start=0.00_01       ,  beta_end=0.02       ,  thresholding=A__       ,  dynamic_thresholding_ratio=0.95       ,  sample_max_value=1.0       ,  prediction_type='epsilon'       ,  variance_type='learned_range'       ,  )

												torch.manual_seed(0				)
												lowerCAmelCase__        =  DDPMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='squaredcos_cap_v2'       ,  beta_start=0.00_01       ,  beta_end=0.02       ,  )

												torch.manual_seed(0				)
												lowerCAmelCase__        =  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				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.get_dummy_components()
												lowerCAmelCase__        =  self.pipeline_class(**A__				)
												pipe.to(A__				)
												pipe.set_progress_bar_config(disable=A__				)

												lowerCAmelCase__        =  self.get_dummy_inputs(A__				)

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

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

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

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

												lowerCAmelCase__  ,    lowerCAmelCase__        =  pipe.encode_prompt(A__				)

												# inputs with prompt converted to embeddings
												lowerCAmelCase__        =  {
												    '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:
																		lowerCAmelCase__        =  image

												if mask_image is not None:
																		lowerCAmelCase__        =  mask_image

												if original_image is not None:
																		lowerCAmelCase__        =  original_image

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

												lowerCAmelCase__        =  pipe(**A__				)[0]

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

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

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

												lowerCAmelCase__        =  self.get_dummy_inputs(A__				)

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

												# inputs with prompt converted to embeddings
												lowerCAmelCase__        =  {
												    '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:
																		lowerCAmelCase__        =  image

												if mask_image is not None:
																		lowerCAmelCase__        =  mask_image

												if original_image is not None:
																		lowerCAmelCase__        =  original_image

												lowerCAmelCase__        =  pipe_loaded(**A__				)[0]

												lowerCAmelCase__        =  np.abs(to_np(A__				) - to_np(A__				)				).max()
												self.assertLess(A__       ,  1E-4				)







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.get_dummy_components()
												lowerCAmelCase__        =  self.pipeline_class(**A__				)
												pipe.to(A__				)
												pipe.set_progress_bar_config(disable=A__				)

												lowerCAmelCase__        =  self.get_dummy_inputs(A__				)
												lowerCAmelCase__        =  pipe(**A__				)[0]

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

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

												lowerCAmelCase__        =  self.get_dummy_inputs(A__				)
												lowerCAmelCase__        =  pipe_loaded(**A__				)[0]

												lowerCAmelCase__        =  np.abs(to_np(A__				) - to_np(A__				)				).max()
												self.assertLess(A__       ,  1E-4				)













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      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

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






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






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

												# bash_script = bash_script.replace("--fp16 ", "")
												lowerCAmelCase__        =  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__        =  ['finetune.py'] + bash_script.split() + args
												with patch.object(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationModule.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		lowerCAmelCase__        =  main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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__        =  {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       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






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

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

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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__        =  {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





