



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


a__   :					Optional[int] 						=      "sshleifer/mar_enro_6_3_student"



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      int)						->					List[Any]:




         """simple docstring"""

         super().setUp()

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


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




         """simple docstring"""

         MarianMTModel.from_pretrained(lowerCAmelCase)



   @slow
   @require_torch_gpu
   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

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

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

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

         # Check metrics
         lowercase__  =	load_json(model.metrics_save_path)
         lowercase__  =	metrics['val'][0]
         lowercase__  =	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}''']      ,					lowerCAmelCase)

         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
         lowercase__  =	os.listdir(lowerCAmelCase)
         lowercase__  =	[x for x in contents if x.endswith('.ckpt')][0]
         lowercase__  =	os.path.join(args.output_dir      ,					lowerCAmelCase)
         lowercase__  =	torch.load(lowerCAmelCase      ,					map_location='cpu')
         lowercase__  =	'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:
               lowercase__  =	{os.path.basename(lowerCAmelCase) 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__(				lowerCamelCase						):

   '''simple docstring'''




   @timeout_decorator.timeout(6_00)
   @slow
   @require_torch_gpu
   def       UpperCAmelCase		(    self	:      Tuple)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
         lowercase__  =	{
             '--fp16_opt_level=O1': '',
             '$MAX_LEN': 1_28,
             '$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
         lowercase__  =	(
             (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py')[1].strip()
         )
         lowercase__  =	bash_script.replace('\\\n'      ,					'').strip().replace('"$@"'      ,					'')
         lowercase__  =	bash_script.replace('--fp16 '      ,					' ')

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

               lowercase__  =	distill_main(lowerCAmelCase)

         # Check metrics
         lowercase__  =	load_json(model.metrics_save_path)
         lowercase__  =	metrics['val'][0]
         lowercase__  =	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}''']      ,					lowerCAmelCase)

         # check lightning ckpt can be loaded and has a reasonable statedict
         lowercase__  =	os.listdir(lowerCAmelCase)
         lowercase__  =	[x for x in contents if x.endswith('.ckpt')][0]
         lowercase__  =	os.path.join(args.output_dir      ,					lowerCAmelCase)
         lowercase__  =	torch.load(lowerCAmelCase      ,					map_location='cpu')
         lowercase__  =	'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:
               lowercase__  =	{os.path.basename(lowerCAmelCase) 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 collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__   :					int 						=      logging.get_logger(__name__)

a__   :					int 						=      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     "marian"
   A     :	Dict												=     ["past_key_values"]
   A     :	List[Any]												=     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self	:      Any      ,					lowerCAmelCase	:      List[Any]=5_81_01      ,					lowerCAmelCase	:      Optional[int]=None      ,					lowerCAmelCase	:      List[str]=10_24      ,					lowerCAmelCase	:      int=12      ,					lowerCAmelCase	:      Union[str, Any]=40_96      ,					lowerCAmelCase	:      int=16      ,					lowerCAmelCase	:      Dict=12      ,					lowerCAmelCase	:      Optional[int]=40_96      ,					lowerCAmelCase	:      str=16      ,					lowerCAmelCase	:      List[Any]=0.0      ,					lowerCAmelCase	:      Optional[Any]=0.0      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Union[str, Any]=True      ,					lowerCAmelCase	:      List[Any]="gelu"      ,					lowerCAmelCase	:      int=10_24      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Optional[int]=0.0      ,					lowerCAmelCase	:      Tuple=0.0      ,					lowerCAmelCase	:      List[Any]=0.02      ,					lowerCAmelCase	:      Union[str, Any]=5_81_00      ,					lowerCAmelCase	:      str=False      ,					lowerCAmelCase	:      Optional[Any]=5_81_00      ,					lowerCAmelCase	:      Union[str, Any]=0      ,					lowerCAmelCase	:      Optional[Any]=0      ,					lowerCAmelCase	:      int=True      ,					**lowerCAmelCase	:      List[str]      ,					)						->					Dict:




         """simple docstring"""

         lowercase__  =	vocab_size
         lowercase__  =	decoder_vocab_size or vocab_size
         lowercase__  =	max_position_embeddings
         lowercase__  =	d_model
         lowercase__  =	encoder_ffn_dim
         lowercase__  =	encoder_layers
         lowercase__  =	encoder_attention_heads
         lowercase__  =	decoder_ffn_dim
         lowercase__  =	decoder_layers
         lowercase__  =	decoder_attention_heads
         lowercase__  =	dropout
         lowercase__  =	attention_dropout
         lowercase__  =	activation_dropout
         lowercase__  =	activation_function
         lowercase__  =	init_std
         lowercase__  =	encoder_layerdrop
         lowercase__  =	decoder_layerdrop
         lowercase__  =	use_cache
         lowercase__  =	encoder_layers
         lowercase__  =	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__  =	share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowerCAmelCase      ,					eos_token_id=lowerCAmelCase      ,					is_encoder_decoder=lowerCAmelCase      ,					decoder_start_token_id=lowerCAmelCase      ,					forced_eos_token_id=lowerCAmelCase      ,					**lowerCAmelCase      ,					)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def       UpperCAmelCase		(    self	:      List[Any])						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])

               if self.use_past:
                     lowercase__  =	{0: 'batch'}
                     lowercase__  =	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
               else:
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}

               if self.use_past:
                     self.fill_with_past_key_values_(lowerCAmelCase      ,					direction='inputs')
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         else:
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                       ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                       ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                   ])

         return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def       UpperCAmelCase		(    self	:      Any)						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super().outputs
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self).outputs
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         # Generate decoder inputs
         lowercase__  =	seq_length if not self.use_past else 1
         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         lowercase__  =	dict(**lowerCAmelCase      ,					**lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,						lowercase__  =	common_inputs['input_ids'].shape
               lowercase__  =	common_inputs['decoder_input_ids'].shape[1]
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase__  =	decoder_seq_length + 3
               lowercase__  =	(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               lowercase__  =	torch.cat(
                   [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase)]      ,					dim=1)

               lowercase__  =	[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__  =	min(lowerCAmelCase      ,					lowerCAmelCase)
               lowercase__  =	max(lowerCAmelCase      ,					lowerCAmelCase) - min_num_layers
               lowercase__  =	'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

               for _ in range(lowerCAmelCase):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                         ))
               # TODO: test this.
               lowercase__  =	encoder_shape if remaining_side_name == 'encoder' else decoder_shape
               for _ in range(lowerCAmelCase      ,					lowerCAmelCase):
                     common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,						lowercase__  =	common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               lowercase__  =	seqlen + 2
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               lowercase__  =	common_inputs['attention_mask'].dtype
               lowercase__  =	torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase      ,					dtype=lowerCAmelCase)]      ,					dim=1)
               lowercase__  =	[
                   (torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
               ]
         return common_inputs


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					fixed_dimension=OnnxConfig.default_fixed_batch      ,					num_token_to_add=0)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase__  =	tokenizer.num_special_tokens_to_add(lowerCAmelCase)
         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					fixed_dimension=OnnxConfig.default_fixed_sequence      ,					num_token_to_add=lowerCAmelCase)

         # Generate dummy inputs according to compute batch and sequence
         lowercase__  =	[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase__  =	dict(tokenizer(lowerCAmelCase      ,					return_tensors=lowerCAmelCase))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowerCAmelCase      ,					batch_size=lowerCAmelCase      ,					seq_length=lowerCAmelCase      ,					is_pair=lowerCAmelCase      ,					framework=lowerCAmelCase)

         else:
               lowercase__  =	self._generate_dummy_inputs_for_causal_lm(
                   lowerCAmelCase      ,					batch_size=lowerCAmelCase      ,					seq_length=lowerCAmelCase      ,					is_pair=lowerCAmelCase      ,					framework=lowerCAmelCase)

         return common_inputs


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str)						->					Optional[int]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super()._flatten_past_key_values_(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self)._flatten_past_key_values_(
                   lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)



   @property
   def       UpperCAmelCase		(    self	:      Optional[int])						->					float:




         """simple docstring"""

         return 1E-4
