




"""simple docstring"""

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							      =     '''sshleifer/mar_enro_6_3_student'''





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			int						):
       super().setUp()

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

 @slow
 @require_torch_gpu
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       MarianMTModel.from_pretrained(_UpperCAmelCase						)



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

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

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

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



 '''simple docstring'''

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

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

             _A     =  distill_main(_UpperCAmelCase						)

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







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

