






'''simple docstring'''



import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__UpperCamelCase      :       Tuple      			=      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
 from run_translation import main  # noqa


set_seed(42)
__UpperCamelCase      :       Union[str, Any]      			=      "sshleifer/student_marian_en_ro_6_1"
__UpperCamelCase      :       str      			=      "sshleifer/tiny-mbart"
@require_torch
class 							__SCREAMING_SNAKE_CASE       (							_lowerCamelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase=False ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=True ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.run_trainer(
             eval_steps=1 ,   max_len=12 ,   model_name=A__ ,   num_train_epochs=1 ,   distributed=A__ ,   extra_args_str=A__ ,   predict_with_generate=A__ ,   do_train=A__ ,   do_eval=A__ ,   do_predict=A__ ,   )
         __a				     =    TrainerState.load_from_json(os.path.join(A__ ,   'trainer_state.json'      )      ).log_history

         if not do_eval:
            return

         __a				     =    [log for log in logs if 'eval_loss' in log.keys()]

         __a				     =    eval_metrics[0]
         if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            __a				     =    eval_metrics[-1]
            assert isinstance(last_step_stats['eval_bleu'] ,   A__      )
            assert not math.isnan(float(last_step_stats['eval_loss']      )      ), "eval_loss must not be `nan`"




      @require_torch_non_multi_gpu
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         self.run_seqaseq_quick()




      @require_torch_multi_gpu
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self.run_seqaseq_quick(distributed=A__      )




      @require_torch_multi_gpu
      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         self.run_seqaseq_quick(distributed=A__      )




      @unittest.skip('Requires an update of the env running those tests'      )
      @require_torch_multi_gpu
      @require_fairscale
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self.run_seqaseq_quick(distributed=A__ ,   extra_args_str='--sharded_ddp simple'      )




      @unittest.skip('Requires an update of the env running those tests'      )
      @require_torch_multi_gpu
      @require_fairscale
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self.run_seqaseq_quick(distributed=A__ ,   extra_args_str='--sharded_ddp simple --fp16'      )




      @unittest.skip('Requires an update of the env running those tests'      )
      @require_torch_multi_gpu
      @require_fairscale
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         self.run_seqaseq_quick(distributed=A__ ,   extra_args_str='--sharded_ddp zero_dp_2' ,   predict_with_generate=A__      )




      @unittest.skip('Requires an update of the env running those tests'      )
      @require_torch_multi_gpu
      @require_fairscale
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         self.run_seqaseq_quick(
             distributed=A__ ,   extra_args_str='--sharded_ddp zero_dp_2 --fp16' ,   predict_with_generate=A__      )




      @require_apex
      @require_torch_gpu
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
         # program and it breaks other tests that run from the same pytest worker, therefore until this is
         # sorted out it must be run only in an external program, that is distributed=True in this
         # test and only under one or more gpus - if we want cpu will need to make a special test
         #
         # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
         # 2nd main() call it botches the future eval.
         #
         self.run_seqaseq_quick(distributed=A__ ,   extra_args_str='--fp16 --fp16_backend=apex'      )
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=A__ ,   extra_args_str='--fp16 --fp16_backend=apex'      )




      @parameterized.expand(['base', 'low', 'high', 'mixed']      )
      @require_torch_multi_gpu
      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         __a				     =    {
             # test with the default log_level - should be info and thus log info once
             'base': {'extra_args_str': '', 'n_matches': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
         }

         __a				     =    experiments[experiment_id]
         __a				     =    {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
         __a				     =    'Running training'
         with CaptureStderr() as cl:
            self.run_seqaseq_quick(**A__ ,   extra_args_str=data['extra_args_str']      )
         __a				     =    len(re.findall(A__ ,   cl.err      )      )
         self.assertEqual(A__ ,   data['n_matches']      )




      @slow
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    self.run_trainer(
             eval_steps=2 ,   max_len=128 ,   model_name=A__ ,   learning_rate=3e-4 ,   num_train_epochs=10 ,   distributed=A__ ,   )

         # Check metrics
         __a				     =    TrainerState.load_from_json(os.path.join(A__ ,   'trainer_state.json'      )      ).log_history
         __a				     =    [log for log in logs if 'eval_loss' in log.keys()]
         __a				     =    eval_metrics[0]
         __a				     =    eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['eval_bleu'] ,   A__      )

         # test if do_predict saves generations and metrics
         __a				     =    os.listdir(A__      )
         __a				     =    {os.path.basename(A__      ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents




      @slow
      @require_bitsandbytes
      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(lowerCamelCase      ) -> Tuple[int, float]:
            __a				     =    '--skip_memory_metrics 0'

            __a				     =    self.run_trainer(
                max_len=128 ,   model_name=A__ ,   learning_rate=3e-4 ,   num_train_epochs=1 ,   optim=A__ ,   distributed=A__ ,   extra_args_str=A__ ,   do_eval=A__ ,   do_predict=A__ ,   n_gpus_to_use=1 ,   )

            # Check metrics
            __a				     =    TrainerState.load_from_json(Path(A__ ,   'trainer_state.json'      )      ).log_history
            __a				     =    int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20      )
            __a				     =    int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20      )

            __a				     =    logs[0]['train_loss']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         __a     ,			__a     ,			__a				     =    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value      )
         __a     ,			__a     ,			__a				     =    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value      )

         __a				     =    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         __a				     =    gpu_peak_mem_orig + gpu_alloc_mem_orig
         __a				     =    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         __a				     =    gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         __a				     =    120

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             A__ ,   A__ ,   'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
             F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
             F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" ,   )

         self.assertGreater(
             A__ ,   A__ ,   'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
             F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
             F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" ,   )

         self.assertEqual(
             A__ ,   A__ ,   F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = 3e-3 ,   lowerCamelCase = "adafactor" ,   lowerCamelCase = False ,   lowerCamelCase = None ,   lowerCamelCase = 0 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   )							->Dict:



         '''simple docstring'''
         __a				     =    self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
         __a				     =    self.get_auto_remove_tmp_dir()
         __a				     =    F"""\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(A__      )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(A__      )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

         __a				     =    F"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(A__      )}\n        """.split()

         __a				     =    '\n            --do_predict\n        '.split()

         __a				     =    []
         if do_train:
            args += args_train

         if do_eval:
            args += args_eval

         if do_predict:
            args += args_predict

         if predict_with_generate:
            args += "--predict_with_generate".split()

         if do_train:
            if optim == "adafactor":
               args += "--adafactor".split()
            else:
               args += F"""--optim {optim}""".split()

         if extra_args_str is not None:
            args += extra_args_str.split()

         if distributed:
            if n_gpus_to_use is None:
               __a				     =    get_gpu_count()
            __a				     =    get_torch_dist_unique_port()
            __a				     =    F"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
            __a				     =    [sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(A__ ,   env=self.get_env()      )
         else:
            __a				     =    ['run_translation.py'] + args
            with patch.object(A__ ,   'argv' ,   A__      ):
               main()

         return output_dir







'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
          "text to read (in English) and returns a waveform object containing the sound."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'      )

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()