'''simple docstring'''

import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







  """simple docstring"""
  lowercase   :      Any       =							'''AutoTokenizer'''
  lowercase   :      List[str]       =							['''tokenizer''']

  lowercase   :      str       =							{
      '''semantic_prompt''': 1,
      '''coarse_prompt''': 2,
      '''fine_prompt''': 2,
  }






  def __init__(   self		,						__A		,						__A=None				) ->     Any:
        super().__init__(A__				)

        _lowerCAmelCase										=speaker_embeddings






  @classmethod
  def 		UpperCamelCase__       (   cls		,						__A		,						__A="speaker_embeddings_path.json"		,						**__A				) ->     List[str]:

        if speaker_embeddings_dict_path is not None:
              _lowerCAmelCase										=get_file_from_repo(
                  A__		,						A__		,						subfolder=kwargs.pop('subfolder'		,						A__				)		,						cache_dir=kwargs.pop('cache_dir'		,						A__				)		,						force_download=kwargs.pop('force_download'		,						A__				)		,						proxies=kwargs.pop('proxies'		,						A__				)		,						resume_download=kwargs.pop('resume_download'		,						A__				)		,						local_files_only=kwargs.pop('local_files_only'		,						A__				)		,						use_auth_token=kwargs.pop('use_auth_token'		,						A__				)		,						revision=kwargs.pop('revision'		,						A__				)		,						)
              if speaker_embeddings_path is None:
                    logger.warning(
                        F'''`{os.path.join(A__		,						A__				)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''				)
                    _lowerCAmelCase										=None
              else:
                    with open(A__				) as speaker_embeddings_json:
                          _lowerCAmelCase										=json.load(A__				)
        else:
              _lowerCAmelCase										=None

        _lowerCAmelCase										=AutoTokenizer.from_pretrained(A__		,						**A__				)

        return cls(tokenizer=A__		,						speaker_embeddings=A__				)






  def 		UpperCamelCase__       (   self		,						__A		,						__A="speaker_embeddings_path.json"		,						__A="speaker_embeddings"		,						__A = False		,						**__A		,						) ->     Dict:
        if self.speaker_embeddings is not None:
              os.makedirs(os.path.join(A__		,						A__		,						'v2'				)		,						exist_ok=A__				)

              _lowerCAmelCase										={}

              _lowerCAmelCase										=save_directory

              for prompt_key in self.speaker_embeddings:
                    if prompt_key != "repo_or_path":
                          _lowerCAmelCase										=self._load_voice_preset(A__				)

                          _lowerCAmelCase										={}
                          for key in self.speaker_embeddings[prompt_key]:
                                np.save(
                                    os.path.join(
                                        embeddings_dict['repo_or_path']		,						A__		,						F'''{prompt_key}_{key}'''				)		,						voice_preset[key]		,						allow_pickle=A__		,						)
                                _lowerCAmelCase										=os.path.join(A__		,						F'''{prompt_key}_{key}.npy'''				)

                          _lowerCAmelCase										=tmp_dict

              with open(os.path.join(A__		,						A__				)		,						'w'				) as fp:
                    json.dump(A__		,						A__				)

        super().save_pretrained(A__		,						A__		,						**A__				)






  def 		UpperCamelCase__       (   self		,						__A = None		,						**__A				) ->     int:
        _lowerCAmelCase										=self.speaker_embeddings[voice_preset]

        _lowerCAmelCase										={}
        for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
              if key not in voice_preset_paths:
                    raise ValueError(
                        F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''				)

              _lowerCAmelCase										=get_file_from_repo(
                  self.speaker_embeddings.get('repo_or_path'		,						'/'				)		,						voice_preset_paths[key]		,						subfolder=kwargs.pop('subfolder'		,						A__				)		,						cache_dir=kwargs.pop('cache_dir'		,						A__				)		,						force_download=kwargs.pop('force_download'		,						A__				)		,						proxies=kwargs.pop('proxies'		,						A__				)		,						resume_download=kwargs.pop('resume_download'		,						A__				)		,						local_files_only=kwargs.pop('local_files_only'		,						A__				)		,						use_auth_token=kwargs.pop('use_auth_token'		,						A__				)		,						revision=kwargs.pop('revision'		,						A__				)		,						)
              if path is None:
                    raise ValueError(
                        F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path'		,						'/'				)		,						voice_preset_paths[key]				)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''				)

              _lowerCAmelCase										=np.load(A__				)

        return voice_preset_dict






  def 		UpperCamelCase__       (   self		,						__A = None				) ->     Optional[int]:
        for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
              if key not in voice_preset:
                    raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''				)

              if not isinstance(voice_preset[key]		,						np.ndarray				):
                    raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'''				)

              if len(voice_preset[key].shape				) != self.preset_shape[key]:
                    raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'''				)






  def __call__(   self		,						__A=None		,						__A=None		,						__A="pt"		,						__A=256		,						__A=False		,						__A=True		,						__A=False		,						**__A		,						) ->     Tuple:
        if voice_preset is not None and not isinstance(A__		,						A__				):
              if (
                  isinstance(A__		,						A__				)
                  and self.speaker_embeddings is not None
                  and voice_preset in self.speaker_embeddings
              ):
                    _lowerCAmelCase										=self._load_voice_preset(A__				)

              else:
                    if isinstance(A__		,						A__				) and not voice_preset.endswith('.npz'				):
                          _lowerCAmelCase										=voice_preset + """.npz"""

                    _lowerCAmelCase										=np.load(A__				)

        if voice_preset is not None:
              self._validate_voice_preset_dict(A__		,						**A__				)
              _lowerCAmelCase										=BatchFeature(data=A__		,						tensor_type=A__				)

        _lowerCAmelCase										=self.tokenizer(
            A__		,						return_tensors=A__		,						padding='max_length'		,						max_length=A__		,						return_attention_mask=A__		,						return_token_type_ids=A__		,						add_special_tokens=A__		,						**A__		,						)

        if voice_preset is not None:
              _lowerCAmelCase										=voice_preset

        return encoded_text






'''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


lowercase_     		= '''sshleifer/mar_enro_6_3_student'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								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 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_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





