


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=7					,   A_=True					,   A_=True					,   A_=False					,   A_=True					,   A_=99					,   A_=32					,   A_=5					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=16					,   A_=2					,   A_=0.02					,   A_=3					,   A_=4					,   A_=None					,   )  ->       int:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=seq_length
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_input_mask
         __UpperCamelCase    							=use_token_type_ids
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=type_vocab_size
         __UpperCamelCase    							=type_sequence_label_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=num_labels
         __UpperCamelCase    							=num_choices
         __UpperCamelCase    							=scope






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

         __UpperCamelCase    							=None
         if self.use_input_mask:
              __UpperCamelCase    							=random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase    							=None
         if self.use_token_type_ids:
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size    )

         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         if self.use_labels:
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.type_sequence_label_size    )
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels    )
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.num_choices    )

         __UpperCamelCase    							=self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def _a					(			self    )  ->       Union[str, Any]:
         return LlamaConfig(
             vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_snake_case					,   initializer_range=self.initializer_range					,   )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Any:
         __UpperCamelCase    							=LlamaModel(config=_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case    )
         __UpperCamelCase    							=model(_snake_case    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       List[str]:
         __UpperCamelCase    							=True
         __UpperCamelCase    							=LlamaModel(_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(
             _snake_case					,   attention_mask=_snake_case					,   encoder_hidden_states=_snake_case					,   encoder_attention_mask=_snake_case					,   )
         __UpperCamelCase    							=model(
             _snake_case					,   attention_mask=_snake_case					,   encoder_hidden_states=_snake_case					,   )
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=LlamaForCausalLM(config=_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case					,   labels=_snake_case    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       Dict:
         __UpperCamelCase    							=True
         __UpperCamelCase    							=True
         __UpperCamelCase    							=LlamaForCausalLM(config=_snake_case    )
         model.to(_snake_case    )
         model.eval()

         # first forward pass
         __UpperCamelCase    							=model(
             _snake_case					,   attention_mask=_snake_case					,   encoder_hidden_states=_snake_case					,   encoder_attention_mask=_snake_case					,   use_cache=_snake_case					,   )
         __UpperCamelCase    							=outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         __UpperCamelCase    							=ids_tensor((self.batch_size, 3)					,   config.vocab_size    )
         __UpperCamelCase    							=ids_tensor((self.batch_size, 3)					,   vocab_size=2    )

         # append to next input_ids and
         __UpperCamelCase    							=torch.cat([input_ids, next_tokens]					,   dim=-1    )
         __UpperCamelCase    							=torch.cat([input_mask, next_mask]					,   dim=-1    )

         __UpperCamelCase    							=model(
             _snake_case					,   attention_mask=_snake_case					,   encoder_hidden_states=_snake_case					,   encoder_attention_mask=_snake_case					,   output_hidden_states=_snake_case					,   )['''hidden_states'''][0]
         __UpperCamelCase    							=model(
             _snake_case					,   attention_mask=_snake_case					,   encoder_hidden_states=_snake_case					,   encoder_attention_mask=_snake_case					,   past_key_values=_snake_case					,   output_hidden_states=_snake_case					,   )['''hidden_states'''][0]

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

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(_snake_case					,   _snake_case					,   atol=1E-3    )    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             __UpperCamelCase			
         )    							=config_and_inputs
         __UpperCamelCase    							={'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    UpperCAmelCase__						:		int      =  (LlamaForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase__						:		int      =  (
        {
            "feature-extraction": LlamaModel,
            "text-classification": LlamaForSequenceClassification,
            "text-generation": LlamaForCausalLM,
            "zero-shot": LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__						:		List[str]      =  False
    UpperCAmelCase__						:		List[Any]      =  False






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=LlamaModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=_snake_case					,   hidden_size=37    )






    def _a					(			self    )  ->       str:
         self.config_tester.run_common_tests()






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_snake_case    )






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase    							=type
              self.model_tester.create_and_check_model(*_snake_case    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							=input_dict['''input_ids''']
         __UpperCamelCase    							=input_ids.ne(1    ).to(_snake_case    )
         __UpperCamelCase    							=ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size    )
         __UpperCamelCase    							=LlamaForSequenceClassification(_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case					,   labels=_snake_case    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							='''single_label_classification'''
         __UpperCamelCase    							=input_dict['''input_ids''']
         __UpperCamelCase    							=input_ids.ne(1    ).to(_snake_case    )
         __UpperCamelCase    							=ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size    )
         __UpperCamelCase    							=LlamaForSequenceClassification(_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case					,   labels=_snake_case    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							='''multi_label_classification'''
         __UpperCamelCase    							=input_dict['''input_ids''']
         __UpperCamelCase    							=input_ids.ne(1    ).to(_snake_case    )
         __UpperCamelCase    							=ids_tensor(
             [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size    ).to(torch.float    )
         __UpperCamelCase    							=LlamaForSequenceClassification(_snake_case    )
         model.to(_snake_case    )
         model.eval()
         __UpperCamelCase    							=model(_snake_case					,   attention_mask=_snake_case					,   labels=_snake_case    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'    )
    def _a					(			self    )  ->       Union[str, Any]:
         pass






    @parameterized.expand([('linear',), ('dynamic',)]    )
    def _a					(			self					,   A_    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=ids_tensor([1, 10]					,   config.vocab_size    )
         __UpperCamelCase    							=ids_tensor([1, int(config.max_position_embeddings * 1.5    )]					,   config.vocab_size    )

         set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
         __UpperCamelCase    							=LlamaModel(_snake_case    )
         original_model.to(_snake_case    )
         original_model.eval()
         __UpperCamelCase    							=original_model(_snake_case    ).last_hidden_state
         __UpperCamelCase    							=original_model(_snake_case    ).last_hidden_state

         set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
         __UpperCamelCase    							={'''type''': scaling_type, '''factor''': 10.0}
         __UpperCamelCase    							=LlamaModel(_snake_case    )
         scaled_model.to(_snake_case    )
         scaled_model.eval()
         __UpperCamelCase    							=scaled_model(_snake_case    ).last_hidden_state
         __UpperCamelCase    							=scaled_model(_snake_case    ).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
              self.assertTrue(torch.allclose(_snake_case					,   _snake_case					,   atol=1E-5    )    )
         else:
              self.assertFalse(torch.allclose(_snake_case					,   _snake_case					,   atol=1E-5    )    )

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(_snake_case					,   _snake_case					,   atol=1E-5    )    )






@require_torch
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
    @slow
    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=[1, 306, 4658, 278, 6593, 310, 2834, 338]
         __UpperCamelCase    							=LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'					,   device_map='auto'    )
         __UpperCamelCase    							=model(torch.tensor([input_ids]    )    )
         # Expected mean on dim = -1
         __UpperCamelCase    							=torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]    )
         torch.testing.assert_close(out.mean(-1    )					,   _snake_case					,   atol=1E-2					,   rtol=1E-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __UpperCamelCase    							=torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]					,   _snake_case					,   atol=1E-5					,   rtol=1E-5    )






    @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
    @slow
    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=[1, 306, 4658, 278, 6593, 310, 2834, 338]
         __UpperCamelCase    							=LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'					,   device_map='auto'    )
         __UpperCamelCase    							=model(torch.tensor(_snake_case    )    )
         # Expected mean on dim = -1
         __UpperCamelCase    							=torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]    )
         torch.testing.assert_close(out.mean(-1    )					,   _snake_case					,   atol=1E-2					,   rtol=1E-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __UpperCamelCase    							=torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]					,   _snake_case					,   atol=1E-5					,   rtol=1E-5    )






    @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
    @slow
    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=[1, 306, 4658, 278, 6593, 310, 2834, 338]
         __UpperCamelCase    							=LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'					,   device_map='auto'    )
         __UpperCamelCase    							=model(torch.tensor(_snake_case    )    )
         # Expected mean on dim = -1
         __UpperCamelCase    							=torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]    )
         torch.testing.assert_close(out.mean(-1    )					,   _snake_case					,   atol=1E-2					,   rtol=1E-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __UpperCamelCase    							=torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]    )
         # fmt: on
         torch.testing.assert_close(out.mean(-1    )					,   _snake_case					,   atol=1E-2					,   rtol=1E-2    )






    @unittest.skip(
        'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'    )
    @slow
    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=[1, 306, 4658, 278, 6593, 310, 2834, 338]
         __UpperCamelCase    							=LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'					,   device_map='auto'    )
         __UpperCamelCase    							=model(torch.tensor(_snake_case    )    )

         __UpperCamelCase    							=torch.tensor(
             [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]					,   dtype=torch.floataa    )
         torch.testing.assert_close(out.mean(-1    )					,   _snake_case					,   atol=1E-2					,   rtol=1E-2    )
         # fmt: off
         __UpperCamelCase    							=torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]					,   _snake_case					,   atol=1E-5					,   rtol=1E-5    )






    @unittest.skip('Model is curently gated'    )
    @slow
    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							='''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
         __UpperCamelCase    							='''Simply put, the theory of relativity states that '''
         __UpperCamelCase    							=LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    )
         __UpperCamelCase    							=tokenizer.encode(_snake_case					,   return_tensors='pt'    )
         __UpperCamelCase    							=LlamaForCausalLM.from_pretrained(
             'meta-llama/Llama-2-13b-chat-hf'					,   device_map='sequential'					,   use_safetensors=_snake_case    )

         # greedy generation outputs
         __UpperCamelCase    							=model.generate(_snake_case					,   max_new_tokens=64					,   top_p=_snake_case					,   temperature=1					,   do_sample=_snake_case    )
         __UpperCamelCase    							=tokenizer.decode(generated_ids[0]					,   skip_special_tokens=_snake_case    )
         self.assertEqual(_snake_case					,   _snake_case    )










"""simple docstring"""







import argparse
import os

# New Code #
import evaluate
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_   						=   16
lowerCAmelCase_   						=   32



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    int				=			load_dataset('''glue'''						,     '''mrpc'''	)

		def tokenize_function(__lowerCamelCase	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__   :    str				=			tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=__lowerCamelCase						,     max_length=__lowerCamelCase	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__   :    str				=			datasets.map(
				    __lowerCamelCase						,     batched=__lowerCamelCase						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowercase__   :    Union[str, Any]				=			tokenized_datasets.rename_column('''label'''						,     '''labels'''	)

		def collate_fn(__lowerCamelCase	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__   :    List[str]				=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__   :    Optional[int]				=			16
				elif accelerator.mixed_precision != "no":
						lowercase__   :    List[Any]				=			8
				else:
						lowercase__   :    int				=			None

				return tokenizer.pad(
				    __lowerCamelCase						,     padding='''longest'''						,     max_length=__lowerCamelCase						,     pad_to_multiple_of=__lowerCamelCase						,     return_tensors='''pt'''						,     )

		# Instantiate dataloaders.
		lowercase__   :    List[Any]				=			DataLoader(
		    tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)
		lowercase__   :    str				=			DataLoader(
		    tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowerCAmelCase_   						=   mocked_dataloaders  # noqa: F811



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,     __lowerCamelCase	) == "1":
				lowercase__   :    List[Any]				=			2
		# Initialize accelerator
		lowercase__   :    Optional[int]				=			Accelerator(cpu=args.cpu						,     mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__   :    str				=			config['''lr''']
		lowercase__   :    str				=			int(config['''num_epochs''']	)
		lowercase__   :    Optional[int]				=			int(config['''seed''']	)
		lowercase__   :    Tuple				=			int(config['''batch_size''']	)

		lowercase__   :    List[Any]				=			evaluate.load('''glue'''						,     '''mrpc'''	)

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=__lowerCamelCase	)
		def inner_training_loop(__lowerCamelCase	):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(__lowerCamelCase	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase__   :    List[str]				=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,     return_dict=__lowerCamelCase	)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowercase__   :    Tuple				=			model.to(accelerator.device	)

				# Instantiate optimizer
				lowercase__   :    List[str]				=			AdamW(params=model.parameters()						,     lr=__lowerCamelCase	)
				lowercase__		,			lowercase__   :    List[Any]				=			get_dataloaders(__lowerCamelCase						,     __lowerCamelCase	)

				# Instantiate scheduler
				lowercase__   :    Optional[int]				=			get_linear_schedule_with_warmup(
				    optimizer=__lowerCamelCase						,     num_warmup_steps=1_00						,     num_training_steps=(len(__lowerCamelCase	) * num_epochs)						,     )

				# 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.
				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Optional[int]				=			accelerator.prepare(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

				# Now we train the model
				for epoch in range(__lowerCamelCase	):
						model.train()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								lowercase__   :    Dict				=			model(**__lowerCamelCase	)
								lowercase__   :    List[Any]				=			outputs.loss
								accelerator.backward(__lowerCamelCase	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								with torch.no_grad():
										lowercase__   :    Tuple				=			model(**__lowerCamelCase	)
								lowercase__   :    Any				=			outputs.logits.argmax(dim=-1	)
								lowercase__		,			lowercase__   :    int				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
								metric.add_batch(
								    predictions=__lowerCamelCase						,     references=__lowerCamelCase						,     )

						lowercase__   :    List[Any]				=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""						,     __lowerCamelCase	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    Optional[int]				=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
		parser.add_argument(
		    '''--mixed_precision'''						,     type=__lowerCamelCase						,     default=__lowerCamelCase						,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']						,     help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.'''						,     )
		parser.add_argument('''--cpu'''						,     action='''store_true'''						,     help='''If passed, will train on the CPU.'''	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Union[str, Any]				=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__lowerCamelCase						,     __lowerCamelCase	)


if __name__ == "__main__":
							main()
