


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__     			=							{
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__     			=							['''GPT2TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__     			=							[
		    '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPT2DoubleHeadsModel''',
		    '''GPT2ForQuestionAnswering''',
		    '''GPT2ForSequenceClassification''',
		    '''GPT2ForTokenClassification''',
		    '''GPT2LMHeadModel''',
		    '''GPT2Model''',
		    '''GPT2PreTrainedModel''',
		    '''load_tf_weights_in_gpt2''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__     			=							[
		    '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFGPT2DoubleHeadsModel''',
		    '''TFGPT2ForSequenceClassification''',
		    '''TFGPT2LMHeadModel''',
		    '''TFGPT2MainLayer''',
		    '''TFGPT2Model''',
		    '''TFGPT2PreTrainedModel''',
		]

try:
		if not is_keras_nlp_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__     			=							['''TFGPT2Tokenizer''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__     			=							['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
		from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
		from .tokenization_gpta import GPTaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_gpta_fast import GPTaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpta import (
				    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTaDoubleHeadsModel,
				    GPTaForQuestionAnswering,
				    GPTaForSequenceClassification,
				    GPTaForTokenClassification,
				    GPTaLMHeadModel,
				    GPTaModel,
				    GPTaPreTrainedModel,
				    load_tf_weights_in_gpta,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gpta import (
				    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGPTaDoubleHeadsModel,
				    TFGPTaForSequenceClassification,
				    TFGPTaLMHeadModel,
				    TFGPTaMainLayer,
				    TFGPTaModel,
				    TFGPTaPreTrainedModel,
				)

		try:
				if not is_keras_nlp_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_gpta_tf import TFGPTaTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
		import sys

		UpperCAmelCase__     			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

