
"""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					(			A__							):






			def  __lowerCAmelCase (					self       )			->     Optional[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=__A       ,   )
						lowerCAmelCase_    :str			     =  f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






			@slow
			@require_torch_gpu
			def  __lowerCAmelCase (					self       )			->     Any:
						MarianMTModel.from_pretrained(__A       )






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

						# bash_script = bash_script.replace("--fp16 ", "")
						lowerCAmelCase_    :Union[str, Any]			     =  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

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

						# Check metrics
						lowerCAmelCase_    :Tuple			     =  load_json(model.metrics_save_path       )
						lowerCAmelCase_    :Union[str, Any]			     =  metrics["""val"""][0]
						lowerCAmelCase_    :Any			     =  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}"""]       ,   __A       )

						self.assertGreater(last_step_stats["""val_avg_gen_time"""]       ,   0.0_1       )
						# 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_    :Optional[Any]			     =  os.listdir(__A       )
						lowerCAmelCase_    :Any			     =  [x for x in contents if x.endswith(""".ckpt"""       )][0]
						lowerCAmelCase_    :int			     =  os.path.join(args.output_dir       ,   __A       )
						lowerCAmelCase_    :str			     =  torch.load(__A       ,   map_location="""cpu"""       )
						lowerCAmelCase_    :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:
									lowerCAmelCase_    :int			     =  {os.path.basename(__A       ) 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					(			A__							):






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

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

									lowerCAmelCase_    :Optional[Any]			     =  distill_main(__A       )

						# Check metrics
						lowerCAmelCase_    :Union[str, Any]			     =  load_json(model.metrics_save_path       )
						lowerCAmelCase_    :List[Any]			     =  metrics["""val"""][0]
						lowerCAmelCase_    :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"] >= 0.0_1

						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}"""]       ,   __A       )

						# check lightning ckpt can be loaded and has a reasonable statedict
						lowerCAmelCase_    :int			     =  os.listdir(__A       )
						lowerCAmelCase_    :str			     =  [x for x in contents if x.endswith(""".ckpt"""       )][0]
						lowerCAmelCase_    :List[str]			     =  os.path.join(args.output_dir       ,   __A       )
						lowerCAmelCase_    :Optional[Any]			     =  torch.load(__A       ,   map_location="""cpu"""       )
						lowerCAmelCase_    :Tuple			     =  """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_    :Tuple			     =  {os.path.basename(__A       ) 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 os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard

						from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline

@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		int:
								with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															UpperCAmelCase :       Optional[Any] 							=		FlaxDiffusionPipeline.from_pretrained(
															    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		,			cache_dir=__snake_case		)

															UpperCAmelCase :       str 							=		[t[-1] for t in os.walk(os.path.join(__snake_case		,			os.listdir(__snake_case		)[0]		,			'''snapshots'''		)		)]
															UpperCAmelCase :       str 							=		[item for sublist in all_root_files for item in sublist]

															# None of the downloaded files should be a PyTorch file even if we have some here:
															# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
															assert not any(f.endswith('''.bin'''		) for f in files		)

@slow
@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       str 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Optional[Any] 							=		4

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Tuple 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 64, 64, 3)
								if jax.device_count() == 8:
															assert np.abs(np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 4.1_51_47_45		) < 1E-3
															assert np.abs(np.abs(__snake_case		,			dtype=np.floataa		).sum() - 4_99_47.8_75		) < 5E-1

								UpperCAmelCase :       Union[str, Any] 							=		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
								assert len(__snake_case		) == num_samples







	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''flax'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Optional[Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Any 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       int 							=		num_samples * [prompt]
								UpperCAmelCase :       Union[str, Any] 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		shard(__snake_case		)

								UpperCAmelCase :       Tuple 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.05_65_24_01)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_38_38_08.2)		) < 5E-1







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       List[str] 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		)

								UpperCAmelCase :       List[str] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[int] 							=		jax.device_count()
								UpperCAmelCase :       List[str] 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       str 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		FlaxDDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			set_alpha_to_one=__snake_case		,			steps_offset=1		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			scheduler=__snake_case		,			safety_checker=__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		scheduler.create_state()

								UpperCAmelCase :       Dict 							=		scheduler_state

								UpperCAmelCase :       str 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       int 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Any 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       str 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[str] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Dict 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.0_45_04_39_45)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_34_76_93.5)		) < 5E-1







	def      A	(						self			:     Any		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       str 							=		jax.random.split(jax.random.PRNGKey(0		)		,			__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			)

								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[str] 							=		shard(__snake_case		)
								UpperCAmelCase :       Any 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       Optional[int] 							=		images[2, 0, 256, 10:17, 1]

								# With memory efficient attention
								UpperCAmelCase							,		UpperCAmelCase :       Any 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			use_memory_efficient_attention=__snake_case		,			)

								UpperCAmelCase :       int 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		shard(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       int 							=		images[2, 0, 256, 10:17, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice		).max() < 1E-2





