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_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        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=__lowercase			,			)
        __UpperCamelCase					:List[str]        =		f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""







    @slow
    @require_torch_gpu
    def       UpperCamelCase__       (     self)	->     List[str]:
        MarianMTModel.from_pretrained(__lowercase)







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

        # bash_script = bash_script.replace("--fp16 ", "")
        __UpperCamelCase					:Optional[int]        =		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					:str        =		['''finetune.py'''] + bash_script.split() + args
        with patch.object(__lowercase			,			'''argv'''			,			__lowercase):
            __UpperCamelCase					:Dict        =		argparse.ArgumentParser()
            __UpperCamelCase					:List[Any]        =		pl.Trainer.add_argparse_args(__lowercase)
            __UpperCamelCase					:Optional[int]        =		SummarizationModule.add_model_specific_args(__lowercase			,			os.getcwd())
            __UpperCamelCase					:Union[str, Any]        =		parser.parse_args()
            __UpperCamelCase					:Union[str, Any]        =		main(__lowercase)

        # Check metrics
        __UpperCamelCase					:Optional[Any]        =		load_json(model.metrics_save_path)
        __UpperCamelCase					:List[Any]        =		metrics['''val'''][0]
        __UpperCamelCase					:str        =		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
        __UpperCamelCase					:Optional[int]        =		os.listdir(__lowercase)
        __UpperCamelCase					:List[Any]        =		[x for x in contents if x.endswith('''.ckpt''')][0]
        __UpperCamelCase					:List[Any]        =		os.path.join(args.output_dir			,			__lowercase)
        __UpperCamelCase					:Optional[int]        =		torch.load(__lowercase			,			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					:List[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      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






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

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

            __UpperCamelCase					:Dict        =		distill_main(__lowercase)

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






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
    import torch


__lowercase										= logging.get_logger(__name__)


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase=False			,			__lowercase=False			,			__lowercase=6.0			,			__lowercase=None			,			__lowercase=False			,			__lowercase=False			,			__lowercase=None			,			__lowercase="fp4"			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:List[str]        =		load_in_abit
        __UpperCamelCase					:Union[str, Any]        =		load_in_abit
        __UpperCamelCase					:str        =		llm_inta_threshold
        __UpperCamelCase					:List[str]        =		llm_inta_skip_modules
        __UpperCamelCase					:Any        =		llm_inta_enable_fpaa_cpu_offload
        __UpperCamelCase					:List[Any]        =		llm_inta_has_fpaa_weight
        __UpperCamelCase					:str        =		bnb_abit_quant_type
        __UpperCamelCase					:Optional[int]        =		bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
            __UpperCamelCase					:Tuple        =		torch.floataa
        elif isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Union[str, Any]        =		getattr(__lowercase			,			__lowercase)
        elif isinstance(__lowercase			,			torch.dtype):
            __UpperCamelCase					:int        =		bnb_abit_compute_dtype
        else:
            raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype''')

        self.post_init()







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        if not isinstance(self.llm_inta_threshold			,			__lowercase):
            raise ValueError('''llm_int8_threshold must be a float''')

        if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules			,			__lowercase):
            raise ValueError('''llm_int8_skip_modules must be a list of strings''')
        if not isinstance(self.llm_inta_enable_fpaa_cpu_offload			,			__lowercase):
            raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean''')

        if not isinstance(self.llm_inta_has_fpaa_weight			,			__lowercase):
            raise ValueError('''llm_int8_has_fp16_weight must be a boolean''')

        if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype			,			torch.dtype):
            raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype''')

        if not isinstance(self.bnb_abit_quant_type			,			__lowercase):
            raise ValueError('''bnb_4bit_quant_type must be a string''')

        if not isinstance(self.bnb_abit_use_double_quant			,			__lowercase):
            raise ValueError('''bnb_4bit_use_double_quant must be a boolean''')

        if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes''')) >= version.parse(
            '''0.39.0'''):
            raise ValueError(
                '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version''')







    def       UpperCamelCase__       (     self)	->     Any:
        return self.load_in_abit or self.load_in_abit







    def       UpperCamelCase__       (     self)	->     List[Any]:
        if self.load_in_abit:
            return "llm_int8"
        elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
            return "fp4"
        elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
            return "nf4"
        else:
            return None







    @classmethod
    def       UpperCamelCase__       (     cls			,			__lowercase			,			__lowercase			,			**__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[int]        =		cls(**__lowercase)

        __UpperCamelCase					:Optional[Any]        =		[]
        for key, value in kwargs.items():
            if hasattr(__lowercase			,			__lowercase):
                setattr(__lowercase			,			__lowercase			,			__lowercase)
                to_remove.append(__lowercase)
        for key in to_remove:
            kwargs.pop(__lowercase			,			__lowercase)

        if return_unused_kwargs:
            return config, kwargs
        else:
            return config







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as writer:
            __UpperCamelCase					:Optional[int]        =		self.to_dict()
            __UpperCamelCase					:Optional[int]        =		json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + '''\n'''

            writer.write(__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Optional[Any]        =		copy.deepcopy(self.__dict__)
        __UpperCamelCase					:Optional[int]        =		str(output['''bnb_4bit_compute_dtype''']).split('''.''')[1]

        return output







    def __repr__(     self)	->     Dict:
        return f"""{self.__class__.__name__} {self.to_json_string()}"""







    def       UpperCamelCase__       (     self			,			__lowercase = True)	->     str:
        if use_diff is True:
            __UpperCamelCase					:Union[str, Any]        =		self.to_diff_dict()
        else:
            __UpperCamelCase					:Dict        =		self.to_dict()
        return json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + "\n"







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Union[str, Any]        =		self.to_dict()

        # get the default config dict
        __UpperCamelCase					:Optional[Any]        =		BitsAndBytesConfig().to_dict()

        __UpperCamelCase					:str        =		{}

        # only serialize values that differ from the default config
        for key, value in config_dict.items():
            if value != default_config_dict[key]:
                __UpperCamelCase					:str        =		value

        return serializable_config_dict





