
'''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


_snake_case				:   str     =				"""sshleifer/mar_enro_6_3_student"""


class 			lowerCAmelCase (				_UpperCamelCase			):




		def     lowercase       (				self      ):
								super().setUp()

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




		@slow
		@require_torch_gpu
		def     lowercase       (				self      ):
								MarianMTModel.from_pretrained(__a      )




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

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

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

								# Check metrics
								_SCREAMING_SNAKE_CASE				= load_json(model.metrics_save_path      )
								_SCREAMING_SNAKE_CASE				= metrics["val"][0]
								_SCREAMING_SNAKE_CASE				= 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"]					,						0.01      )
								# 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
								_SCREAMING_SNAKE_CASE				= os.listdir(__a      )
								_SCREAMING_SNAKE_CASE				= [x for x in contents if x.endswith(".ckpt"      )][0]
								_SCREAMING_SNAKE_CASE				= os.path.join(args.output_dir					,						__a      )
								_SCREAMING_SNAKE_CASE				= torch.load(__a					,						map_location="cpu"      )
								_SCREAMING_SNAKE_CASE				= "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:
														_SCREAMING_SNAKE_CASE				= {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 			lowerCAmelCase (				_UpperCamelCase			):




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

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

														_SCREAMING_SNAKE_CASE				= distill_main(__a      )

								# Check metrics
								_SCREAMING_SNAKE_CASE				= load_json(model.metrics_save_path      )
								_SCREAMING_SNAKE_CASE				= metrics["val"][0]
								_SCREAMING_SNAKE_CASE				= 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}']					,						__a      )

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						_snake_case				:   str     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)