
"""simple docstring"""





from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   for param, grad_param in zip(model_a.parameters(),		model_b.parameters()  ):
      if not param.requires_grad:
         continue
      if not did_step:
         # Grads should not be in sync
         assert (
             torch.allclose(param.grad,		grad_param.grad  ) is False
         ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
      else:
         # Grads should be in sync
         assert (
             torch.allclose(param.grad,		grad_param.grad  ) is True
         ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=True  ):
   model.train()
   UpperCAmelCase_					:					int			     =    model(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    F.mse_loss(__lowerCamelCase,		target.to(output.device  )  )
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=False  ):
   set_seed(42  )
   UpperCAmelCase_					:					Dict			     =    RegressionModel()
   UpperCAmelCase_					:					Optional[Any]			     =    deepcopy(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    RegressionDataset(length=80  )
   UpperCAmelCase_					:					List[Any]			     =    DataLoader(__lowerCamelCase,		batch_size=16  )
   model.to(accelerator.device  )
   if sched:
      UpperCAmelCase_					:					Any			     =    AdamW(params=model.parameters(),		lr=1E-3  )
      UpperCAmelCase_					:					str			     =    AdamW(params=ddp_model.parameters(),		lr=1E-3  )
      UpperCAmelCase_					:					str			     =    LambdaLR(__lowerCamelCase,		lr_lambda=lambda __lowerCamelCase  : epoch**0.65  )
      UpperCAmelCase_					:					List[str]			     =    LambdaLR(__lowerCamelCase,		lr_lambda=lambda __lowerCamelCase  : epoch**0.65  )
   # Make a copy of `model`
   if sched:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    accelerator.prepare(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
   else:
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[Any]			     =    accelerator.prepare(__lowerCamelCase,		__lowerCamelCase  )
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader







def 	__a     (		__lowerCamelCase  ):
   # Test when on a single CPU or GPU that the context manager does nothing
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    get_training_setup(__lowerCamelCase  )
   # Use a single batch
   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    next(iter(__lowerCamelCase  )  ).values()
   for iteration in range(3  ):
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    accelerator.gather((ddp_input, ddp_target)  )
      UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(__lowerCamelCase  ):
            step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      else:
         # Sync grads
         step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

      # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
      check_model_parameters(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         assert torch.allclose(
             param.grad,		ddp_param.grad  ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

      # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      UpperCAmelCase_					:					Optional[Any]			     =    ddp_input[torch.randperm(len(__lowerCamelCase  )  )]







def 	__a     (		__lowerCamelCase  ):
   # Test on distributed setup that context manager behaves properly
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    get_training_setup(__lowerCamelCase  )
   # Use a single batch
   UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    next(iter(__lowerCamelCase  )  ).values()
   for iteration in range(3  ):
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    accelerator.gather((ddp_input, ddp_target)  )
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(__lowerCamelCase  ):
            step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      else:
         # Sync grads
         step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

      # DDP model and model should only be in sync when not (iteration % 2 == 0)
      for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         if iteration % 2 == 0:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad,		ddp_param.grad  ) is False
            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
         else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad,		ddp_param.grad  ) is True
            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      UpperCAmelCase_					:					Dict			     =    ddp_input[torch.randperm(len(__lowerCamelCase  )  )]







def 	__a     (		__lowerCamelCase=False,		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					Tuple			     =    Accelerator(
       split_batches=__lowerCamelCase,		dispatch_batches=__lowerCamelCase,		gradient_accumulation_steps=2  )
   # Test that context manager behaves properly
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    get_training_setup(__lowerCamelCase  )
   for iteration, batch in enumerate(__lowerCamelCase  ):
      UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    batch.values()
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    accelerator.gather((ddp_input, ddp_target)  )
      UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      # Do "gradient accumulation" (noop)
      with accelerator.accumulate(__lowerCamelCase  ):
         step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

      # DDP model and model should only be in sync when not (iteration % 2 == 0)
      for param, ddp_param in zip(model.parameters(),		ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         if ((iteration + 1) % 2 == 0) or (iteration == len(__lowerCamelCase  ) - 1):
            # Grads should be in sync
            assert (
                torch.allclose(param.grad,		ddp_param.grad  ) is True
            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
         else:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad,		ddp_param.grad  ) is False
            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      UpperCAmelCase_					:					int			     =    ddp_input[torch.randperm(len(__lowerCamelCase  )  )]
   GradientState._reset_state()







def 	__a     (		__lowerCamelCase=False,		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					List[Any]			     =    Accelerator(
       split_batches=__lowerCamelCase,		dispatch_batches=__lowerCamelCase,		gradient_accumulation_steps=2  )
   # Test that context manager behaves properly
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    get_training_setup(__lowerCamelCase,		__lowerCamelCase  )
   for iteration, batch in enumerate(__lowerCamelCase  ):
      UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    batch.values()
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[Any]			     =    accelerator.gather((ddp_input, ddp_target)  )
      UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      model.train()
      ddp_model.train()
      step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      opt.step()

      if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__lowerCamelCase  )):
         if split_batches:
            sched.step()
         else:
            for _ in range(accelerator.num_processes  ):
               sched.step()
      opt.zero_grad()
      # Perform gradient accumulation under wrapper
      with accelerator.accumulate(__lowerCamelCase  ):
         step_model(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
         ddp_opt.step()
         ddp_sched.step()
         ddp_opt.zero_grad()

      # Learning rates should be the same
      assert (
          opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
      ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
      UpperCAmelCase_					:					str			     =    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__lowerCamelCase  ))
      if accelerator.num_processes > 1:
         check_model_parameters(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
   GradientState._reset_state()







def 	__a     (		):
   UpperCAmelCase_					:					Dict			     =    Accelerator()

   UpperCAmelCase_					:					Tuple			     =    RegressionDataset(length=80  )
   UpperCAmelCase_					:					str			     =    DataLoader(__lowerCamelCase,		batch_size=16  )
   UpperCAmelCase_					:					Optional[Any]			     =    RegressionDataset(length=96  )
   UpperCAmelCase_					:					List[Any]			     =    DataLoader(__lowerCamelCase,		batch_size=16  )
   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    accelerator.prepare(__lowerCamelCase,		__lowerCamelCase  )
   assert accelerator.gradient_state.active_dataloader is None
   for iteration, _ in enumerate(__lowerCamelCase  ):
      assert id(accelerator.gradient_state.active_dataloader  ) == id(__lowerCamelCase  )
      if iteration < len(__lowerCamelCase  ) - 1:
         assert not accelerator.gradient_state.end_of_dataloader
         if iteration == 1:
            for batch_num, _ in enumerate(__lowerCamelCase  ):
               assert id(accelerator.gradient_state.active_dataloader  ) == id(__lowerCamelCase  )
               if batch_num < len(__lowerCamelCase  ) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
               else:
                  assert accelerator.gradient_state.end_of_dataloader
      else:
         assert accelerator.gradient_state.end_of_dataloader
   assert accelerator.gradient_state.active_dataloader is None







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    Accelerator()
   UpperCAmelCase_					:					int			     =    accelerator.state
   if state.local_process_index == 0:
      print("**Test `accumulate` gradient accumulation with dataloader break**"  )
   test_dataloader_break()
   if state.distributed_type == DistributedType.NO:
      if state.local_process_index == 0:
         print("**Test NOOP `no_sync` context manager**"  )
      test_noop_sync(__lowerCamelCase  )
   if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
      if state.local_process_index == 0:
         print("**Test Distributed `no_sync` context manager**"  )
      test_distributed_sync(__lowerCamelCase  )
   if state.distributed_type == DistributedType.MULTI_GPU:
      for split_batch in [True, False]:
         for dispatch_batches in [True, False]:
            if state.local_process_index == 0:
               print(
                   "**Test `accumulate` gradient accumulation, ",		f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""",		)
            test_gradient_accumulation(__lowerCamelCase,		__lowerCamelCase  )

    # Currently will break on torch 2.0 +, need to investigate why
   if is_torch_version("<",		"2.0"  ) or state.distributed_type == DistributedType.NO:
      if state.local_process_index == 0:
         print(
             "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",		"`split_batches=False`, `dispatch_batches=False`**",		)
      test_gradient_accumulation_with_opt_and_scheduler()
      if state.distributed_type == DistributedType.MULTI_GPU:
         for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
               if not split_batch and not dispatch_batches:
                  continue
               if state.local_process_index == 0:
                  print(
                      "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",		f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""",		)
               test_gradient_accumulation_with_opt_and_scheduler(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase  ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()




"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel


class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=30				,   lowercase_=2				,   lowercase_=3				,   lowercase_=True				,   lowercase_=True				,   lowercase_=32				,   lowercase_=5				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=10				,   lowercase_=0.02				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    parent
     UpperCAmelCase_					:					List[str]			     =    batch_size
     UpperCAmelCase_					:					Union[str, Any]			     =    image_size
     UpperCAmelCase_					:					List[str]			     =    patch_size
     UpperCAmelCase_					:					Union[str, Any]			     =    num_channels
     UpperCAmelCase_					:					Optional[int]			     =    is_training
     UpperCAmelCase_					:					Dict			     =    use_labels
     UpperCAmelCase_					:					Any			     =    hidden_size
     UpperCAmelCase_					:					Optional[Any]			     =    num_hidden_layers
     UpperCAmelCase_					:					Optional[Any]			     =    num_attention_heads
     UpperCAmelCase_					:					Dict			     =    intermediate_size
     UpperCAmelCase_					:					Optional[Any]			     =    hidden_act
     UpperCAmelCase_					:					Optional[Any]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Dict			     =    type_sequence_label_size
     UpperCAmelCase_					:					Optional[Any]			     =    initializer_range

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					Dict			     =    ViTConfig(
         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				,   )

     return config, pixel_values







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    FlaxViTModel(config=lowercase_  )
     UpperCAmelCase_					:					int			     =    model(lowercase_  )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase_					:					Optional[Any]			     =    (self.image_size, self.image_size)
     UpperCAmelCase_					:					List[Any]			     =    (self.patch_size, self.patch_size)
     UpperCAmelCase_					:					str			     =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, num_patches + 1, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.type_sequence_label_size
     UpperCAmelCase_					:					Tuple			     =    FlaxViTForImageClassification(config=lowercase_  )
     UpperCAmelCase_					:					str			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)  )

     # test greyscale images
     UpperCAmelCase_					:					Any			     =    1
     UpperCAmelCase_					:					Optional[int]			     =    FlaxViTForImageClassification(lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Tuple			     =    config_and_inputs
     UpperCAmelCase_					:					Union[str, Any]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




@require_flax
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    FlaxViTModelTester(self  )
     UpperCAmelCase_					:					Dict			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_					:					List[str]			     =    ["pixel_values"]
        self.assertListEqual(arg_names[:1]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Tuple			     =    model_class(lowercase_  )

           @jax.jit
           def model_jitted(lowercase_				,   **lowercase_  ):
              return model(pixel_values=lowercase_				,   **lowercase_  )

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					Union[str, Any]			     =    model_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					Tuple			     =    model_jitted(**lowercase_  ).to_tuple()

           self.assertEqual(len(lowercase_  )				,   len(lowercase_  )  )
           for jitted_output, output in zip(lowercase_				,   lowercase_  ):
              self.assertEqual(jitted_output.shape				,   output.shape  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    model_class_name.from_pretrained("google/vit-base-patch16-224"  )
        UpperCAmelCase_					:					List[str]			     =    model(np.ones((1, 3, 224, 224)  )  )
        self.assertIsNotNone(lowercase_  )


