'''simple docstring'''
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
 from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class _lowerCAmelCase			(	snake_case					):







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   self.config_class(**self.inputs_dict     )
        self.parent.assertTrue(hasattr(lowercase  ,    '''hidden_sizes'''     )     )
        self.parent.assertTrue(hasattr(lowercase  ,    '''num_encoder_blocks'''     )     )





class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=13  ,    lowercase=64  ,    lowercase=3  ,    lowercase=4  ,    lowercase=[2, 2, 2, 2]  ,    lowercase=[8, 4, 2, 1]  ,    lowercase=[16, 32, 64, 128]  ,    lowercase=[1, 4, 8, 16]  ,    lowercase=False  ,    lowercase=True  ,    lowercase="gelu"  ,    lowercase=0.1  ,    lowercase=0.02  ,    lowercase=3  ,    lowercase=None  ,    ):
        lowercase				:							int									=   parent
        lowercase				:							List[Any]									=   batch_size
        lowercase				:							List[Any]									=   image_size
        lowercase				:							str									=   num_channels
        lowercase				:							Dict									=   num_encoder_blocks
        lowercase				:							Tuple									=   sr_ratios
        lowercase				:							Union[str, Any]									=   depths
        lowercase				:							Tuple									=   hidden_sizes
        lowercase				:							Any									=   downsampling_rates
        lowercase				:							Any									=   is_training
        lowercase				:							Tuple									=   use_labels
        lowercase				:							Any									=   hidden_act
        lowercase				:							List[Any]									=   hidden_dropout_prob
        lowercase				:							Optional[int]									=   initializer_range
        lowercase				:							int									=   num_labels
        lowercase				:							Optional[Any]									=   scope







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[Any]									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        lowercase				:							List[Any]									=   None
        if self.use_labels:
          lowercase				:							str									=   ids_tensor([self.batch_size, self.image_size, self.image_size]  ,    self.num_labels     )

        lowercase				:							List[Any]									=   PoolFormerConfig(
            image_size=self.image_size  ,    num_channels=self.num_channels  ,    num_encoder_blocks=self.num_encoder_blocks  ,    depths=self.depths  ,    hidden_sizes=self.hidden_sizes  ,    hidden_act=self.hidden_act  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    initializer_range=self.initializer_range  ,    )

        return config, pixel_values, labels







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							int									=   PoolFormerModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							List[Any]									=   model(lowercase     )
        lowercase				:							int									=   self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Dict									=   config_and_inputs
        lowercase				:							int									=   {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
class _lowerCAmelCase			(	snake_case							,   snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						List[str]	 =       (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
      UpperCAmelCase_	:						str	 =       (
          {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_	:						Any	 =       False
      UpperCAmelCase_	:						Tuple	 =       False
      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						Union[str, Any]	 =       False
      UpperCAmelCase_	:						Tuple	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Tuple									=   PoolFormerModelTester(self     )
        lowercase				:							Dict									=   PoolFormerConfigTester(self  ,    config_class=lowercase     )







      def      lowerCamelCase__      (  self     ):
        self.config_tester.run_common_tests()







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      @unittest.skip('''PoolFormer does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        def check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     ):
          lowercase				:							Any									=   model_class(lowercase     )
          model.to(lowercase     )
          model.eval()

          with torch.no_grad():
            lowercase				:							Dict									=   model(**self._prepare_for_class(lowercase  ,    lowercase     )     )

          lowercase				:							Any									=   outputs.hidden_states

          lowercase				:							Dict									=   self.model_tester.num_encoder_blocks
          self.assertEqual(len(lowercase     )  ,    lowercase     )

          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-3:]     )  ,    [
                  self.model_tester.hidden_sizes[0],
                  self.model_tester.image_size // 4,
                  self.model_tester.image_size // 4,
              ]  ,    )

        lowercase				,      lowercase				:							str									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							Union[str, Any]									=   True
          check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowercase				:							List[str]									=   True

          check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        if not self.model_tester.is_training:
          return

        lowercase				,      lowercase				:							Optional[int]									=   self.model_tester.prepare_config_and_inputs_for_common()
        lowercase				:							List[str]									=   True

        for model_class in self.all_model_classes:
          if model_class in get_values(lowercase     ):
            continue
          lowercase				:							List[Any]									=   model_class(lowercase     )
          model.to(lowercase     )
          model.train()
          lowercase				:							Optional[Any]									=   self._prepare_for_class(lowercase  ,    lowercase  ,    return_labels=lowercase     )
          lowercase				:							Optional[int]									=   model(**lowercase     ).loss
          loss.backward()







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							Dict									=   model_class(lowercase     )
          lowercase				:							List[str]									=   inspect.signature(model.forward     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase				:							Optional[int]									=   [*signature.parameters.keys()]

          lowercase				:							Dict									=   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]  ,    lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							List[Any]									=   PoolFormerModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







def 				__UpperCamelCase (					)							->	str:
  lowercase				:							int									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
  return image






@require_torch
class _lowerCAmelCase			(	unittest.TestCase					):







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   PoolFormerImageProcessor()
        lowercase				:							int									=   PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''     ).to(lowercase     )

        lowercase				:							List[str]									=   image_processor(images=prepare_img()  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							str									=   model(**lowercase     )

        # verify the logits
        lowercase				:							Union[str, Any]									=   torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape  ,    lowercase     )

        lowercase				:							int									=   torch.tensor([-0.6113, 0.1685, -0.0492]     ).to(lowercase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,    lowercase  ,    atol=1e-4     )     )
'''simple docstring'''
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 (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # 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 (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   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				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   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 (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        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      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )