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






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowercase										= logging.get_logger(__name__)
logging.set_verbosity_info()



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







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''self_attn''': '''ngram_self_attn''',
        '''cross_attn''': '''encoder_attn''',
        '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
        '''feed_forward_layer_norm''': '''final_layer_norm''',
        '''feed_forward''': '''''',
        '''intermediate''': '''fc1''',
        '''output''': '''fc2''',
        '''key_proj''': '''k_proj''',
        '''query_proj''': '''q_proj''',
        '''value_proj''': '''v_proj''',
        '''word_embeddings''': '''embed_tokens''',
        '''embeddings_layer_norm''': '''emb_layer_norm''',
        '''relative_pos_embeddings''': '''relative_linear''',
        '''ngram_embeddings''': '''ngram_input_embed''',
        '''position_embeddings''': '''embed_positions''',
    }

    for key in loading_info["missing_keys"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

        __UpperCamelCase					:Optional[Any]        =		False
        for attribute in attributes:
            if attribute in mapping:
                __UpperCamelCase					:str        =		mapping[attribute]
                if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ) and len(SCREAMING_SNAKE_CASE     ) > 0:
                    __UpperCamelCase					:Optional[int]        =		attribute
            elif hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                __UpperCamelCase					:List[Any]        =		attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                __UpperCamelCase					:Tuple        =		old_model.weight
                logger.info(f"""{attribute} is initialized."""     )
                __UpperCamelCase					:Union[str, Any]        =		True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                __UpperCamelCase					:Union[str, Any]        =		old_model.bias
                logger.info(f"""{attribute} is initialized"""     )
                __UpperCamelCase					:List[Any]        =		True
                break
            elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE  ,      '''in_proj_weight'''     ):
                __UpperCamelCase					:str        =		old_model.in_proj_weight.shape[0] // 3
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.in_proj_weight[:embed_dim, :]     )
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_bias[:embed_dim]     )

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        if not is_key_init:
            raise ValueError(f"""{key} was not correctly initialized!"""     )

    print(f"""Saving model to {pytorch_dump_folder_path}"""     )
    prophet.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





