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						:   Optional[int]         =			'sshleifer/mar_enro_6_3_student'



class        lowercase_   (					__snake_case		):



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

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



							@slow
							@require_torch_gpu
							def 	UpperCamelCase	(						self    ):
													MarianMTModel.from_pretrained(lowercase_    )



							@slow
							@require_torch_gpu
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					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
													_snake_case						:					Tuple    =   (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"    )[1].strip()
													_snake_case						:					List[str]    =   bash_script.replace("\\\n"				,    ""    ).strip().replace("\"$@\""				,    ""    )
													for k, v in env_vars_to_replace.items():
																			_snake_case						:					Optional[int]    =   bash_script.replace(lowercase_				,    str(lowercase_    )    )
													_snake_case						:					Union[str, Any]    =   self.get_auto_remove_tmp_dir()

													# bash_script = bash_script.replace("--fp16 ", "")
													_snake_case						:					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

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

													# Check metrics
													_snake_case						:					Union[str, Any]    =   load_json(model.metrics_save_path    )
													_snake_case						:					List[str]    =   metrics["val"][0]
													_snake_case						:					Optional[int]    =   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}"""]				,    lowercase_    )

													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
													_snake_case						:					Dict    =   os.listdir(lowercase_    )
													_snake_case						:					List[str]    =   [x for x in contents if x.endswith(".ckpt"    )][0]
													_snake_case						:					Optional[Any]    =   os.path.join(args.output_dir				,    lowercase_    )
													_snake_case						:					List[str]    =   torch.load(lowercase_				,    map_location="cpu"    )
													_snake_case						:					Dict    =   "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:
																			_snake_case						:					str    =   {os.path.basename(lowercase_    ) 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_   (					__snake_case		):



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

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

																			_snake_case						:					Optional[int]    =   distill_main(lowercase_    )

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

													# check lightning ckpt can be loaded and has a reasonable statedict
													_snake_case						:					str    =   os.listdir(lowercase_    )
													_snake_case						:					Dict    =   [x for x in contents if x.endswith(".ckpt"    )][0]
													_snake_case						:					Any    =   os.path.join(args.output_dir				,    lowercase_    )
													_snake_case						:					List[Any]    =   torch.load(lowercase_				,    map_location="cpu"    )
													_snake_case						:					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:
																			_snake_case						:					Any    =   {os.path.basename(lowercase_    ) 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
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)