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


__lowercase										= '''sshleifer/mar_enro_6_3_student'''


class      lowerCamelCase_					(       __snake_case			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     List[str]:
        super().setUp()

        __UpperCamelCase					:Union[str, Any]        =		cached_path(
            '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''			,			extract_compressed_file=UpperCamelCase__			,			)
        __UpperCamelCase					:Optional[Any]        =		f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







    @slow
    @require_torch_gpu
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        MarianMTModel.from_pretrained(UpperCamelCase__)







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

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

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

        # Check metrics
        __UpperCamelCase					:str        =		load_json(model.metrics_save_path)
        __UpperCamelCase					:Optional[int]        =		metrics["val"][0]
        __UpperCamelCase					: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}"""]			,			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
        __UpperCamelCase					:Dict        =		os.listdir(UpperCamelCase__)
        __UpperCamelCase					:List[str]        =		[x for x in contents if x.endswith('''.ckpt''')][0]
        __UpperCamelCase					:Any        =		os.path.join(args.output_dir			,			UpperCamelCase__)
        __UpperCamelCase					:int        =		torch.load(UpperCamelCase__			,			map_location='''cpu''')
        __UpperCamelCase					: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:
            __UpperCamelCase					:int        =		{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      lowerCamelCase_					(       __snake_case			):






    '''simple docstring'''






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

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

            __UpperCamelCase					:Tuple        =		distill_main(UpperCamelCase__)

        # Check metrics
        __UpperCamelCase					:Any        =		load_json(model.metrics_save_path)
        __UpperCamelCase					:Tuple        =		metrics["val"][0]
        __UpperCamelCase					:int        =		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
        __UpperCamelCase					:Tuple        =		os.listdir(UpperCamelCase__)
        __UpperCamelCase					:Tuple        =		[x for x in contents if x.endswith('''.ckpt''')][0]
        __UpperCamelCase					:str        =		os.path.join(args.output_dir			,			UpperCamelCase__)
        __UpperCamelCase					:int        =		torch.load(UpperCamelCase__			,			map_location='''cpu''')
        __UpperCamelCase					:Optional[int]        =		"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					:Tuple        =		{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






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

    for _ in range(vertex_count - 1     ):
        for j in range(SCREAMING_SNAKE_CASE     ):
            __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
                __UpperCamelCase					:Any        =		distance[u] + w

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        __lowercase			,	__lowercase			,	__lowercase										= (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        __lowercase										= {'''src''': src, '''dst''': dest, '''weight''': weight}

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





