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

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=100  ,    lowercase=13  ,    lowercase=30  ,    lowercase=2  ,    lowercase=3  ,    lowercase=True  ,    lowercase=True  ,    lowercase=32  ,    lowercase=4  ,    lowercase=4  ,    lowercase=37  ,    lowercase="gelu"  ,    lowercase=0.1  ,    lowercase=0.1  ,    lowercase=10  ,    lowercase=0.02  ,    lowercase=3  ,    lowercase=None  ,    lowercase=[0, 1, 2, 3]  ,    ):
        lowercase				:							Union[str, Any]									=   parent
        lowercase				:							List[Any]									=   100
        lowercase				:							str									=   batch_size
        lowercase				:							Dict									=   image_size
        lowercase				:							Dict									=   patch_size
        lowercase				:							Union[str, Any]									=   num_channels
        lowercase				:							Dict									=   is_training
        lowercase				:							str									=   use_labels
        lowercase				:							Dict									=   hidden_size
        lowercase				:							List[str]									=   num_hidden_layers
        lowercase				:							Dict									=   num_attention_heads
        lowercase				:							int									=   intermediate_size
        lowercase				:							Tuple									=   hidden_act
        lowercase				:							Optional[Any]									=   hidden_dropout_prob
        lowercase				:							Dict									=   attention_probs_dropout_prob
        lowercase				:							Tuple									=   type_sequence_label_size
        lowercase				:							Union[str, Any]									=   initializer_range
        lowercase				:							Dict									=   scope
        lowercase				:							List[Any]									=   out_indices
        lowercase				:							Optional[Any]									=   num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase				:							List[str]									=   (image_size // patch_size) ** 2
        lowercase				:							Any									=   num_patches + 1







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

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

        lowercase				:							List[Any]									=   self.get_config()

        return config, pixel_values, labels, pixel_labels







      def      lowerCamelCase__      (  self     ):
        return DataaVecVisionConfig(
            vocab_size=self.vocab_size  ,    image_size=self.image_size  ,    patch_size=self.patch_size  ,    num_channels=self.num_channels  ,    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  ,    is_decoder=lowercase  ,    initializer_range=self.initializer_range  ,    out_indices=self.out_indices  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Optional[int]									=   DataaVecVisionModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							str									=   model(lowercase     )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase				:							str									=   (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, num_patches + 1, self.hidden_size)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   self.type_sequence_label_size
        lowercase				:							List[str]									=   DataaVecVisionForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Dict									=   model(lowercase  ,    labels=lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							List[Any]									=   self.num_labels
        lowercase				:							Optional[int]									=   DataaVecVisionForSemanticSegmentation(lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Union[str, Any]									=   model(lowercase     )
        self.parent.assertEqual(
            result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )
        lowercase				:							List[str]									=   model(lowercase  ,    labels=lowercase     )
        self.parent.assertEqual(
            result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )







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





@require_torch
class _lowerCAmelCase			(	snake_case							,   snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Any	 =       (
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_	:						Dict	 =       (
          {
              "feature-extraction": DataaVecVisionModel,
              "image-classification": DataaVecVisionForImageClassification,
              "image-segmentation": DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_	:						List[str]	 =       False
      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						Optional[int]	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   DataaVecVisionModelTester(self     )
        lowercase				:							int									=   ConfigTester(
            self  ,    config_class=lowercase  ,    has_text_modality=lowercase  ,    hidden_size=37     )







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







      def      lowerCamelCase__      (  self     ):
        # Data2VecVision does not use inputs_embeds
        pass







      @require_torch_multi_gpu
      @unittest.skip(
          reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							int									=   model_class(lowercase     )
          self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)     )
          lowercase				:							Optional[int]									=   model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowercase  ,    nn.Linear     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Optional[int]									=   self.model_tester.prepare_config_and_inputs_for_common()

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

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







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







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*lowercase     )







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

        lowercase				,      lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs_for_common()
        lowercase				:							Any									=   True

        for model_class in self.all_model_classes:
          if model_class in [*get_values(lowercase     )]:
            continue

          lowercase				:							Optional[int]									=   model_class(lowercase     )
          model.to(lowercase     )
          model.train()
          lowercase				:							List[Any]									=   self._prepare_for_class(lowercase  ,    lowercase  ,    return_labels=lowercase     )
          lowercase				:							Dict									=   model(**lowercase     ).loss
          loss.backward()







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							int									=   self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
          return

        lowercase				:							List[Any]									=   False
        lowercase				:							Optional[int]									=   True

        for model_class in self.all_model_classes:
          if model_class in [*get_values(lowercase     )] or not model_class.supports_gradient_checkpointing:
            continue
          # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
          # this can then be incorporated into _prepare_for_class in test_modeling_common.py
          elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
            lowercase				,      lowercase				,      lowercase				,      lowercase				:							Any									=   inputs_dict['''pixel_values'''].shape
            lowercase				:							Optional[Any]									=   torch.zeros(
                [self.model_tester.batch_size, height, width]  ,    device=lowercase     ).long()
          lowercase				:							Optional[Any]									=   model_class(lowercase     )
          model.gradient_checkpointing_enable()
          model.to(lowercase     )
          model.train()
          lowercase				:							Dict									=   self._prepare_for_class(lowercase  ,    lowercase  ,    return_labels=lowercase     )
          lowercase				:							List[Any]									=   model(**lowercase     ).loss
          loss.backward()







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Tuple									=   _config_zero_init(lowercase     )
        for model_class in self.all_model_classes:
          lowercase				:							Dict									=   model_class(config=lowercase     )
          for name, param in model.named_parameters():
            # we skip lambda parameters as these require special initial values
            # determined by config.layer_scale_init_value
            if "lambda" in name:
              continue
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()  ,    [0.0, 1.0]  ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase=2e-4  ,    lowercase="outputs"  ,    lowercase=None     ):
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							Optional[int]									=   DataaVecVisionModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







def 				__UpperCamelCase (					)							->	Tuple:
  lowercase				:							Union[str, Any]									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
  return image






@require_torch
@require_vision
class _lowerCAmelCase			(	unittest.TestCase					):







      @cached_property
      def      lowerCamelCase__      (  self     ):
        return (
            BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''     ) if is_vision_available() else None
        )







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''     ).to(
            lowercase     )

        lowercase				:							Dict									=   self.default_image_processor
        lowercase				:							str									=   prepare_img()
        lowercase				:							str									=   image_processor(images=lowercase  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Optional[int]									=   model(**lowercase     )
        lowercase				:							Dict									=   outputs.logits

        # verify the logits
        lowercase				:							Any									=   torch.Size((1, 1000)     )
        self.assertEqual(logits.shape  ,    lowercase     )

        lowercase				:							Optional[int]									=   torch.tensor([0.3277, -0.1395, 0.0911]     ).to(lowercase     )

        self.assertTrue(torch.allclose(logits[0, :3]  ,    lowercase  ,    atol=1e-4     )     )

        lowercase				:							Optional[Any]									=   [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
        self.assertEqual(logits[0].topk(2     ).indices.cpu().tolist()  ,    lowercase     )
'''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     )