




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase    :		Any			   =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output









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    :		Tuple			   =       "sshleifer/mar_enro_6_3_student"







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       super().setUp()

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

 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     int:

       """simple docstring"""






       MarianMTModel.from_pretrained(A_  )





 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   {
           '$MAX_LEN': 64,
           '$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
       lowerCamelCase_							=   (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'  )[1].strip()
       lowerCamelCase_							=   bash_script.replace('\\\n'						,							''  ).strip().replace('"$@"'						,							''  )
       for k, v in env_vars_to_replace.items():
             lowerCamelCase_							=   bash_script.replace(A_						,							str(A_  )  )
       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()

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

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

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



 '''simple docstring'''


 @timeout_decorator.timeout(600  )
 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
       lowerCamelCase_							=   {
           '--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
       lowerCamelCase_							=   (
           (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'  )[1].strip()
       )
       lowerCamelCase_							=   bash_script.replace('\\\n'						,							''  ).strip().replace('"$@"'						,							''  )
       lowerCamelCase_							=   bash_script.replace('--fp16 '						,							' '  )

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

             lowerCamelCase_							=   distill_main(A_  )

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



