'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					LEDConfig
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					{}
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					"""gelu"""
 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Any=13      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=7      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=False      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=99      ,       SCREAMING_SNAKE_CASE__   :	Dict=32      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=2      ,       SCREAMING_SNAKE_CASE__   :	int=4      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=37      ,       SCREAMING_SNAKE_CASE__   :	int=0.1      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0.1      ,       SCREAMING_SNAKE_CASE__   :	int=20      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=2      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=1      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0      ,       SCREAMING_SNAKE_CASE__   :	List[str]=4      ,       )  ->    str:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						seq_length
  __lowerCAmelCase											=						is_training
  __lowerCAmelCase											=						use_labels
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size

  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						max_position_embeddings
  __lowerCAmelCase											=						eos_token_id
  __lowerCAmelCase											=						pad_token_id
  __lowerCAmelCase											=						bos_token_id
  __lowerCAmelCase											=						attention_window

  # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
  # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
  # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
  # because its local attention only attends to `self.attention_window` and one before and one after
  __lowerCAmelCase											=						self.attention_window + 2

  # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
  # the `test_attention_outputs` and `test_hidden_states_output` tests
  __lowerCAmelCase											=						(
      self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
  )
 def a					(      self   :	Dict							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length - 1]      ,       self.vocab_size							)
  __lowerCAmelCase											=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)      ,       1							)
  __lowerCAmelCase											=						tf.concat([input_ids, eos_tensor]      ,       axis=1							)

  __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

  __lowerCAmelCase											=						self.config_cls(
      vocab_size=self.vocab_size      ,       d_model=self.hidden_size      ,       encoder_layers=self.num_hidden_layers      ,       decoder_layers=self.num_hidden_layers      ,       encoder_attention_heads=self.num_attention_heads      ,       decoder_attention_heads=self.num_attention_heads      ,       encoder_ffn_dim=self.intermediate_size      ,       decoder_ffn_dim=self.intermediate_size      ,       dropout=self.hidden_dropout_prob      ,       attention_dropout=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       eos_token_ids=[2]      ,       bos_token_id=self.bos_token_id      ,       pad_token_id=self.pad_token_id      ,       decoder_start_token_id=self.pad_token_id      ,       attention_window=self.attention_window      ,       **self.config_updates      ,       )
  __lowerCAmelCase											=						prepare_led_inputs_dict(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						tf.concat(
      [tf.zeros_like(SCREAMING_SNAKE_CASE__							)[:, :-1], tf.ones_like(SCREAMING_SNAKE_CASE__							)[:, -1:]]      ,       axis=-1      ,       )
  __lowerCAmelCase											=						global_attention_mask
  return config, inputs_dict





 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    str:
  __lowerCAmelCase											=						TFLEDModel(config=SCREAMING_SNAKE_CASE__							).get_decoder()
  __lowerCAmelCase											=						inputs_dict["""input_ids"""]

  __lowerCAmelCase											=						input_ids[:1, :]
  __lowerCAmelCase											=						inputs_dict["""attention_mask"""][:1, :]
  __lowerCAmelCase											=						1

  # first forward pass
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       use_cache=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						outputs.to_tuple()

  # create hypothetical next token and extent to next_input_ids
  __lowerCAmelCase											=						ids_tensor((self.batch_size, 3)      ,       config.vocab_size							)
  __lowerCAmelCase											=						tf.cast(ids_tensor((self.batch_size, 3)      ,       2							)      ,       tf.inta							)

  # append to next input_ids and
  __lowerCAmelCase											=						tf.concat([input_ids, next_tokens]      ,       axis=-1							)
  __lowerCAmelCase											=						tf.concat([attention_mask, next_attn_mask]      ,       axis=-1							)

  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__							)[0]
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       past_key_values=SCREAMING_SNAKE_CASE__							)[0]

  self.parent.assertEqual(next_tokens.shape[1]      ,       output_from_past.shape[1]							)

  # select random slice
  __lowerCAmelCase											=						int(ids_tensor((1,)      ,       output_from_past.shape[-1]							)							)
  __lowerCAmelCase											=						output_from_no_past[:, -3:, random_slice_idx]
  __lowerCAmelCase											=						output_from_past[:, :, random_slice_idx]

  # test that outputs are equal for slice
  tf.debugging.assert_near(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-3							)







def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       Union[str, Any]						,		snake_case_       :       Tuple						,		snake_case_       :       str=None						,		snake_case_       :       Tuple=None						,		snake_case_       :       str=None						,		snake_case_       :       Any=None						,		)					->     Dict:






 '''simple docstring'''

 if attention_mask is None:
  __lowerCAmelCase											=						tf.cast(tf.math.not_equal(snake_case_						,		config.pad_token_id      )						,		tf.inta      )
 if decoder_attention_mask is None:
  __lowerCAmelCase											=						tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape						,		dtype=tf.inta      ),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,		config.pad_token_id      )						,		tf.inta      ),
      ]						,		axis=-1						,		)
 if head_mask is None:
  __lowerCAmelCase											=						tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
 if decoder_head_mask is None:
  __lowerCAmelCase											=						tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
     "decoder_input_ids": decoder_input_ids,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
 }







@require_tf
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _SCREAMING_SNAKE_CASE     :       List[str]      	=					(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					True
 _SCREAMING_SNAKE_CASE     :       str      	=					False
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       str      	=					False
 def a					(      self   :	Optional[Any]							)  ->    int:
  __lowerCAmelCase											=						TFLEDModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Dict:
  self.config_tester.run_common_tests()
 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    int:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase											=						tf.zeros_like(inputs_dict["""attention_mask"""]							)
  __lowerCAmelCase											=						2
  __lowerCAmelCase											=						tf.where(
      tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices      ,       1      ,       inputs_dict["""global_attention_mask"""]      ,       )

  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						self.model_tester.seq_length
  __lowerCAmelCase											=						self.model_tester.encoder_seq_length

  def check_decoder_attentions_output(SCREAMING_SNAKE_CASE__   :	Tuple							):
   __lowerCAmelCase											=						outputs.decoder_attentions
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
   self.assertListEqual(
       list(decoder_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, seq_length, seq_length]      ,       )

  def check_encoder_attentions_output(SCREAMING_SNAKE_CASE__   :	Tuple							):
   __lowerCAmelCase											=						[t.numpy() for t in outputs.encoder_attentions]
   __lowerCAmelCase											=						[t.numpy() for t in outputs.encoder_global_attentions]
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
   self.assertListEqual(
       list(attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, seq_length, seq_length]      ,       )
   self.assertListEqual(
       list(global_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]      ,       )

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						False
   __lowerCAmelCase											=						False
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						model(self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
   __lowerCAmelCase											=						len(SCREAMING_SNAKE_CASE__							)
   self.assertEqual(config.output_hidden_states      ,       SCREAMING_SNAKE_CASE__							)
   check_encoder_attentions_output(SCREAMING_SNAKE_CASE__							)

   if self.is_encoder_decoder:
    __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase											=						model(self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
    self.assertEqual(config.output_hidden_states      ,       SCREAMING_SNAKE_CASE__							)
    check_decoder_attentions_output(SCREAMING_SNAKE_CASE__							)

   # Check that output attentions can also be changed via the config
   del inputs_dict["output_attentions"]
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						model(self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
   self.assertEqual(config.output_hidden_states      ,       SCREAMING_SNAKE_CASE__							)
   check_encoder_attentions_output(SCREAMING_SNAKE_CASE__							)

   # Check attention is always last and order is fine
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						model(self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)

   self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,       len(SCREAMING_SNAKE_CASE__							)							)
   self.assertEqual(model.config.output_hidden_states      ,       SCREAMING_SNAKE_CASE__							)
   check_encoder_attentions_output(SCREAMING_SNAKE_CASE__							)
 @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""							)
 def a					(      self   :	Union[str, Any]							)  ->    Any:
  pass





 def a					(      self   :	str							)  ->    List[str]:
  # TODO: Head-masking not yet implement
  pass







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[Any]:






 '''simple docstring'''

 return tf.constant(snake_case_						,		dtype=tf.intaa      )


_A			:      Union[str, Any]					      =				1e-4







@slow
@require_tf
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	List[str]							)  ->    Any:
  __lowerCAmelCase											=						TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

  # change to intended input here
  __lowerCAmelCase											=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						prepare_led_inputs_dict(model.config      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)[0]
  __lowerCAmelCase											=						(1, 10_24, 7_68)
  self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE__							)
  # change to expected output here
  __lowerCAmelCase											=						tf.convert_to_tensor(
      [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]      ,       )
  tf.debugging.assert_near(output[:, :3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-3							)





 def a					(      self   :	int							)  ->    Optional[int]:
  __lowerCAmelCase											=						TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

  # change to intended input here
  __lowerCAmelCase											=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						prepare_led_inputs_dict(model.config      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)[0]
  __lowerCAmelCase											=						(1, 10_24, model.config.vocab_size)
  self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE__							)
  # change to expected output here
  __lowerCAmelCase											=						tf.convert_to_tensor(
      [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]      ,       )
  tf.debugging.assert_near(output[:, :3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-3      ,       rtol=1e-3							)







'''simple docstring'''

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


_A			:      Optional[Any]					      =				16
_A			:      Union[str, Any]					      =				32







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     str:






 '''simple docstring'''

 return int(x / 2**20      )







class 				_lowercase     :


 '''simple docstring'''






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





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







def      UpperCamelCase_						(					snake_case_       :       Accelerator						,		snake_case_       :       int = 16						,		snake_case_       :       str = "bert-base-cased"						,		snake_case_       :       int = 3_20						,		snake_case_       :       int = 1_60						,		)					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained(snake_case_      )
 __lowerCAmelCase											=						load_dataset(
     """glue"""						,		"""mrpc"""						,		split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""}      )

 def tokenize_function(snake_case_       :       List[Any]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						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
 __lowerCAmelCase											=						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
 __lowerCAmelCase											=						tokenized_datasets.rename_column("""label"""						,		"""labels"""      )

 def collate_fn(snake_case_       :       List[Any]      ):
  # 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=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

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

 return train_dataloader, eval_dataloader







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						Accelerator()

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

 set_seed(snake_case_      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						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)
 __lowerCAmelCase											=						AutoModelForSequenceClassification.from_pretrained(snake_case_						,		return_dict=snake_case_      )

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

 if accelerator.state.deepspeed_plugin is not None:
  __lowerCAmelCase											=						accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  __lowerCAmelCase											=						1
 __lowerCAmelCase											=						(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
 ):
  __lowerCAmelCase											=						get_linear_schedule_with_warmup(
      optimizer=snake_case_						,		num_warmup_steps=0						,		num_training_steps=snake_case_						,		)
 else:
  __lowerCAmelCase											=						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.
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						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
 __lowerCAmelCase											=						0
 # We also need to keep track of the stating epoch so files are named properly
 __lowerCAmelCase											=						0

 # Now we train the model
 __lowerCAmelCase											=						{}
 for epoch in range(snake_case_						,		snake_case_      ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(snake_case_      ):
    __lowerCAmelCase											=						model(**snake_case_      )
    __lowerCAmelCase											=						outputs.loss
    __lowerCAmelCase											=						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      )      )      )
  __lowerCAmelCase											=						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      UpperCamelCase_						(					)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						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=3_20						,		help="""Number of training examples to use."""						,		)
 parser.add_argument(
     """--n_val"""						,		type=snake_case_						,		default=1_60						,		help="""Number of validation examples to use."""						,		)
 parser.add_argument(
     """--num_epochs"""						,		type=snake_case_						,		default=1						,		help="""Number of train epochs."""						,		)
 __lowerCAmelCase											=						parser.parse_args()
 __lowerCAmelCase											=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
 training_function(snake_case_						,		snake_case_      )


if __name__ == "__main__":
 main()






