import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import SpeechaTextFeatureExtractor

_SCREAMING_SNAKE_CASE										=						random.Random()






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:Optional[int]=1.0   ,	snake_case__			:List[Any]=None   ,	snake_case__			:List[str]=None)						->   Dict:
       if rng is None:
              _A        =  global_rng

       _A        =  []
       for batch_idx in range(shape[0]):
              values.append([])
              for _ in range(shape[1]):
                     values[-1].append(rng.random() * scale)

       return values



@require_torch
@require_torchaudio
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=4_00		,				lowerCAmelCase_=20_00		,				lowerCAmelCase_=24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1_60_00		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				)  ->					Tuple:
        _A        =  parent
        _A        =  batch_size
        _A        =  min_seq_length
        _A        =  max_seq_length
        _A        =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A        =  feature_size
        _A        =  num_mel_bins
        _A        =  padding_value
        _A        =  sampling_rate
        _A        =  return_attention_mask
        _A        =  do_normalize



 def      UpperCAmelCase							(					self							)  ->					Dict:
        return {
            "feature_size": self.feature_size,
            "num_mel_bins": self.num_mel_bins,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=False		,				lowerCAmelCase_=False							)  ->					Optional[int]:
        def _flatten(lowerCAmelCase_							):
               return list(itertools.chain(*lowerCAmelCase_							)							)

        if equal_length:
               _A        =  [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
        else:
               # make sure that inputs increase in size
               _A        =  [
                   floats_list((x, self.feature_size)							)
                   for x in range(self.min_seq_length		,				self.max_seq_length		,				self.seq_length_diff							)
               ]
        if numpify:
               _A        =  [np.asarray(lowerCAmelCase_							) for x in speech_inputs]
        return speech_inputs



@require_torch
@require_torchaudio
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:List[str]    			=		SpeechaTextFeatureExtractor if is_speech_available() else None



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  SpeechaTextFeatureExtractionTester(self							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[str]:
        self.assertTrue(np.all(np.mean(lowerCAmelCase_		,				axis=0							) < 1E-3							)							)
        self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase_		,				axis=0							) - 1							) < 1E-3							)							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        # create three inputs of length 800, 1000, and 1200
        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]
        _A        =  [np.asarray(lowerCAmelCase_							) for speech_input in speech_inputs]

        # Test feature size
        _A        =  feature_extractor(lowerCAmelCase_		,				padding=lowerCAmelCase_		,				return_tensors="""np"""							).input_features
        self.assertTrue(input_features.ndim == 3							)
        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

        # Test not batched input
        _A        =  feature_extractor(speech_inputs[0]		,				return_tensors="""np"""							).input_features
        _A        =  feature_extractor(np_speech_inputs[0]		,				return_tensors="""np"""							).input_features
        self.assertTrue(np.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=1E-3							)							)

        # Test batched
        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							).input_features
        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
               self.assertTrue(np.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=1E-3							)							)

        # Test 2-D numpy arrays are batched.
        _A        =  [floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
        _A        =  np.asarray(lowerCAmelCase_							)
        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							).input_features
        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
               self.assertTrue(np.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=1E-3							)							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]

        _A        =  ["""longest""", """max_length""", """do_not_pad"""]
        _A        =  [None, 16, None]
        for max_length, padding in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  feature_extractor(
                   lowerCAmelCase_		,				padding=lowerCAmelCase_		,				max_length=lowerCAmelCase_		,				return_attention_mask=lowerCAmelCase_							)
               _A        =  inputs.input_features
               _A        =  inputs.attention_mask
               _A        =  [np.sum(lowerCAmelCase_							) for x in attention_mask]

               self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
               self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
               self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]

        _A        =  ["""longest""", """max_length""", """do_not_pad"""]
        _A        =  [None, 16, None]
        for max_length, padding in zip(lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  feature_extractor(
                   lowerCAmelCase_		,				max_length=lowerCAmelCase_		,				padding=lowerCAmelCase_		,				return_tensors="""np"""		,				return_attention_mask=lowerCAmelCase_							)
               _A        =  inputs.input_features
               _A        =  inputs.attention_mask
               _A        =  [np.sum(lowerCAmelCase_							) for x in attention_mask]

               self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
               self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6							)
               self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
               self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6							)
               self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]
        _A        =  feature_extractor(
            lowerCAmelCase_		,				padding="""max_length"""		,				max_length=4		,				truncation=lowerCAmelCase_		,				return_tensors="""np"""		,				return_attention_mask=lowerCAmelCase_		,				)
        _A        =  inputs.input_features
        _A        =  inputs.attention_mask
        _A        =  np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1]							)
        self._check_zero_mean_unit_variance(input_features[2]							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]
        _A        =  feature_extractor(
            lowerCAmelCase_		,				padding="""longest"""		,				max_length=4		,				truncation=lowerCAmelCase_		,				return_tensors="""np"""		,				return_attention_mask=lowerCAmelCase_		,				)
        _A        =  inputs.input_features
        _A        =  inputs.attention_mask
        _A        =  np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
        self._check_zero_mean_unit_variance(input_features[2]							)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape		,				(3, 4, 24)							)

        _A        =  [floats_list((1, x)							)[0] for x in range(8_00		,				14_00		,				2_00							)]
        _A        =  feature_extractor(
            lowerCAmelCase_		,				padding="""longest"""		,				max_length=16		,				truncation=lowerCAmelCase_		,				return_tensors="""np"""		,				return_attention_mask=lowerCAmelCase_		,				)
        _A        =  inputs.input_features
        _A        =  inputs.attention_mask
        _A        =  np.sum(attention_mask == 1		,				axis=1							)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
        self._check_zero_mean_unit_variance(input_features[2]							)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape		,				(3, 6, 24)							)



 def      UpperCAmelCase							(					self							)  ->					str:
        import torch

        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  np.random.rand(1_00		,				32							).astype(np.floataa							)
        _A        =  np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
               _A        =  feature_extractor.pad([{"""input_features""": inputs}]		,				return_tensors="""np"""							)
               self.assertTrue(np_processed.input_features.dtype == np.floataa							)
               _A        =  feature_extractor.pad([{"""input_features""": inputs}]		,				return_tensors="""pt"""							)
               self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[str]:
        from datasets import load_dataset

        _A        =  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,				"""clean"""		,				split="""validation"""							)
        # automatic decoding with librispeech
        _A        =  ds.sort("""id"""							).select(range(lowerCAmelCase_							)							)[:num_samples]["""audio"""]

        return [x["array"] for x in speech_samples]



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # fmt: off
        _A        =  np.array([
            -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
            -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
            -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
        ]							)
        # fmt: on

        _A        =  self._load_datasamples(1							)
        _A        =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""pt"""							).input_features
        self.assertEquals(input_features.shape		,				(1, 5_84, 24)							)
        self.assertTrue(np.allclose(input_features[0, 0, :30]		,				lowerCAmelCase_		,				atol=1E-4							)							)





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        _A        =  torch.cuda.memory_allocated()
        return self



 def __exit__(					self		,				*lowerCAmelCase_							)  ->					Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        _A        =  torch.cuda.memory_allocated()
        _A        =  torch.cuda.max_memory_allocated()
        _A        =  bamb(self.end - self.begin							)
        _A        =  bamb(self.peak - self.begin							)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

       def tokenize_function(snake_case__			:Optional[int]):
              # max_length=None => use the model max length (it's actually the default)
              _A        =  tokenizer(examples["""sentence1"""]   ,	examples["""sentence2"""]   ,	truncation=snake_case__   ,	max_length=snake_case__)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       _A        =  datasets.map(
           snake_case__   ,	batched=snake_case__   ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,	load_from_cache_file=snake_case__)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _A        =  tokenized_datasets.rename_column("""label"""   ,	"""labels""")

       def collate_fn(snake_case__			:List[str]):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(snake_case__   ,	padding="""max_length"""   ,	max_length=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

       # Instantiate dataloaders.
       _A        =  DataLoader(
           tokenized_datasets["""train"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)
       _A        =  DataLoader(
           tokenized_datasets["""validation"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _A        =  config["""lr"""]
       _A        =  int(config["""num_epochs"""])
       _A        =  int(config["""seed"""])
       _A        =  int(config["""batch_size"""])
       _A        =  args.model_name_or_path

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _A        =  AutoModelForSequenceClassification.from_pretrained(snake_case__   ,	return_dict=snake_case__)

       # Instantiate optimizer
       _A        =  (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _A        =  optimizer_cls(params=model.parameters()   ,	lr=snake_case__)

       if accelerator.state.deepspeed_plugin is not None:
              _A        =  accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              _A        =  1
       _A        =  (len(snake_case__) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              _A        =  get_linear_schedule_with_warmup(
                  optimizer=snake_case__   ,	num_warmup_steps=0   ,	num_training_steps=snake_case__   ,	)
       else:
              _A        =  DummyScheduler(snake_case__   ,	total_num_steps=snake_case__   ,	warmup_num_steps=0)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _A   ,  _A   ,  _A   ,  _A   ,  _A        =  accelerator.prepare(
           snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)

       # We need to keep track of how many total steps we have iterated over
       _A        =  0
       # We also need to keep track of the stating epoch so files are named properly
       _A        =  0

       # Now we train the model
       _A        =  {}
       for epoch in range(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  loss / gradient_accumulation_steps
                            accelerator.backward(snake_case__)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin)))
              accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used))
              accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked))
              accelerator.print(
                  """Total Peak Memory consumed during the train (max): {}""".format(
                      tracemalloc.peaked + bamb(tracemalloc.begin)))
              _A        =  tracemalloc.peaked + bamb(tracemalloc.begin)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir   ,	"""peak_memory_utilization.json""")   ,	"""w""") as f:
                     json.dump(snake_case__   ,	snake_case__)






def  snake_case  (       )						->   Optional[int]:
       _A        =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
       parser.add_argument(
           """--model_name_or_path"""   ,	type=snake_case__   ,	default="""bert-base-cased"""   ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,	required=snake_case__   ,	)
       parser.add_argument(
           """--output_dir"""   ,	type=snake_case__   ,	default="""."""   ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,	)
       parser.add_argument(
           """--peak_memory_upper_bound"""   ,	type=snake_case__   ,	default=snake_case__   ,	help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""   ,	)
       parser.add_argument(
           """--n_train"""   ,	type=snake_case__   ,	default=320   ,	help="""Number of training examples to use."""   ,	)
       parser.add_argument(
           """--n_val"""   ,	type=snake_case__   ,	default=160   ,	help="""Number of validation examples to use."""   ,	)
       parser.add_argument(
           """--num_epochs"""   ,	type=snake_case__   ,	default=1   ,	help="""Number of train epochs."""   ,	)
       _A        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




