




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


_lowerCAmelCase						:   Optional[int]		=   "sshleifer/mar_enro_6_3_student"
class 			_UpperCamelCase      (				_UpperCamelCase		):
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[Any]:
       super().setUp()

       UpperCAmelCase__							 =						cached_path(
           "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz" ,      extract_compressed_file=__a ,      )
       UpperCAmelCase__							 =						f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
      @slow
      @require_torch_gpu
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[int]:
       MarianMTModel.from_pretrained(__a      )







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

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

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

       # Check metrics
       UpperCAmelCase__							 =						load_json(model.metrics_save_path      )
       UpperCAmelCase__							 =						metrics["val"][0]
       UpperCAmelCase__							 =						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
       UpperCAmelCase__							 =						os.listdir(__a      )
       UpperCAmelCase__							 =						[x for x in contents if x.endswith(".ckpt"      )][0]
       UpperCAmelCase__							 =						os.path.join(args.output_dir ,      __a      )
       UpperCAmelCase__							 =						torch.load(__a ,      map_location="cpu"      )
       UpperCAmelCase__							 =						"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:
        UpperCAmelCase__							 =						{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 			_UpperCamelCase      (				_UpperCamelCase		):
      @timeout_decorator.timeout(600      )
      @slow
      @require_torch_gpu
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       UpperCAmelCase__							 =						f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
       UpperCAmelCase__							 =						{
           "--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
       UpperCAmelCase__							 =						(
           (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"      )[1].strip()
       )
       UpperCAmelCase__							 =						bash_script.replace("\\\n" ,      ""      ).strip().replace("\"$@\"" ,      ""      )
       UpperCAmelCase__							 =						bash_script.replace("--fp16 " ,      " "      )

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

        UpperCAmelCase__							 =						distill_main(__a      )

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











# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





