





'''simple docstring'''






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      if is_vision_available():
            from transformers import OneFormerImageProcessor
            from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
            from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
      from PIL import Image


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"   ):


  """simple docstring"""

  with open(hf_hub_download(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''   )  ,      '''r'''   ) as f:
    _UpperCamelCase       			=json.load(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			={}
  _UpperCamelCase       			=[]
  _UpperCamelCase       			=[]
  for key, info in class_info.items():
    _UpperCamelCase       			=info['''name''']
    class_names.append(info['''name''']   )
    if info["isthing"]:
      thing_ids.append(int(__SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase       			=thing_ids
  _UpperCamelCase       			=class_names
  return metadata







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Union[str, Any]=30							,	UpperCamelCase__    :					List[str]=400							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					Union[str, Any]=True							,	UpperCamelCase__    :					str=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					str=10							,	UpperCamelCase__    :					Optional[int]=False							,	UpperCamelCase__    :					Optional[Any]=255							,	UpperCamelCase__    :					Any="shi-labs/oneformer_demo"							,	UpperCamelCase__    :					Dict="ade20k_panoptic.json"							,	UpperCamelCase__    :					Dict=10							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize
    _UpperCamelCase       			={'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std
    _UpperCamelCase       			=class_info_file
    _UpperCamelCase       			=prepare_metadata(UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=num_text
    _UpperCamelCase       			=repo_path

    # for the post_process_functions
    _UpperCamelCase       			=2
    _UpperCamelCase       			=10
    _UpperCamelCase       			=10
    _UpperCamelCase       			=3
    _UpperCamelCase       			=4
    _UpperCamelCase       			=num_labels
    _UpperCamelCase       			=do_reduce_labels
    _UpperCamelCase       			=ignore_index


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Union[str, Any]:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "num_labels": self.num_labels,
        "do_reduce_labels": self.do_reduce_labels,
        "ignore_index": self.ignore_index,
        "class_info_file": self.class_info_file,
        "metadata": self.metadata,
        "num_text": self.num_text,
    }


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					List[Any]=False  )     ->						List[str]:
    if not batched:
      _UpperCamelCase       			=image_inputs[0]
      if isinstance(UpperCamelCase__							,	Image.Image  ):
        _UpperCamelCase			,     _UpperCamelCase       			=image.size
      else:
        _UpperCamelCase			,     _UpperCamelCase       			=image.shape[1], image.shape[2]
      if w < h:
        _UpperCamelCase       			=int(self.size['''shortest_edge'''] * h / w  )
        _UpperCamelCase       			=self.size['''shortest_edge''']
      elif w > h:
        _UpperCamelCase       			=self.size['''shortest_edge''']
        _UpperCamelCase       			=int(self.size['''shortest_edge'''] * w / h  )
      else:
        _UpperCamelCase       			=self.size['''shortest_edge''']
        _UpperCamelCase       			=self.size['''shortest_edge''']

    else:
      _UpperCamelCase       			=[]
      for image in image_inputs:
        _UpperCamelCase			,     _UpperCamelCase       			=self.get_expected_values([image]  )
        expected_values.append((expected_height, expected_width)  )
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[0]  )[0]
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[1]  )[1]

    return expected_height, expected_width




  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    return OneFormerForUniversalSegmentationOutput(
        # +1 for null class
        class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)  )							,	masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)  )							,	)







@require_torch
@require_vision
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  lowerCAmelCase_					=				image_processing_class


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Tuple:
    _UpperCamelCase       			=OneFormerImageProcessorTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    return self.image_processing_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Dict  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''ignore_index'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''class_info_file'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''num_text'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''repo_path'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''metadata'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_reduce_labels'''  )  )


  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    pass


  def   UpperCamelCase__      (				self    :					Any  )     ->						Optional[Any]:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	Image.Image  )

    # Test not batched input
    _UpperCamelCase       			=image_processor(image_inputs[0]							,	['''semantic''']							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  def   UpperCamelCase__      (				self    :					int  )     ->						int:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=image_processor(image_inputs[0]							,	['''semantic''']							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Union[str, Any]:
    # Initialize image_processor
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=image_processor(image_inputs[0]							,	['''semantic''']							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processing_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processing_tester.batch_size,
            self.image_processing_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					List[str]=False							,	UpperCamelCase__    :					Optional[Any]=False							,	UpperCamelCase__    :					List[Any]="np"  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # prepare image and target
    _UpperCamelCase       			=self.image_processing_tester.num_labels
    _UpperCamelCase       			=None
    _UpperCamelCase       			=None
    _UpperCamelCase       			=prepare_image_inputs(self.image_processing_tester							,	equal_resolution=UpperCamelCase__  )
    if with_segmentation_maps:
      _UpperCamelCase       			=num_labels
      if is_instance_map:
        _UpperCamelCase       			=list(range(UpperCamelCase__  )  ) * 2
        _UpperCamelCase       			=dict(enumerate(UpperCamelCase__  )  )
      _UpperCamelCase       			=[
          np.random.randint(0							,	high * 2							,	(img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
      ]
      if segmentation_type == "pil":
        _UpperCamelCase       			=[Image.fromarray(UpperCamelCase__  ) for annotation in annotations]

    _UpperCamelCase       			=image_processor(
        UpperCamelCase__							,	['''semantic'''] * len(UpperCamelCase__  )							,	UpperCamelCase__							,	return_tensors='''pt'''							,	instance_id_to_semantic_id=UpperCamelCase__							,	pad_and_return_pixel_mask=UpperCamelCase__							,	)

    return inputs


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						str:
    pass


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						List[Any]:
    def common(UpperCamelCase__    :					Tuple=False							,	UpperCamelCase__    :					List[Any]=None  ):
      _UpperCamelCase       			=self.comm_get_image_processor_inputs(
          with_segmentation_maps=UpperCamelCase__							,	is_instance_map=UpperCamelCase__							,	segmentation_type=UpperCamelCase__  )

      _UpperCamelCase       			=inputs['''mask_labels''']
      _UpperCamelCase       			=inputs['''class_labels''']
      _UpperCamelCase       			=inputs['''pixel_values''']
      _UpperCamelCase       			=inputs['''text_inputs''']

      # check the batch_size
      for mask_label, class_label, text_input in zip(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  ):
        self.assertEqual(mask_label.shape[0]							,	class_label.shape[0]  )
        # this ensure padding has happened
        self.assertEqual(mask_label.shape[1:]							,	pixel_values.shape[2:]  )
        self.assertEqual(len(UpperCamelCase__  )							,	self.image_processing_tester.num_text  )

    common()
    common(is_instance_map=UpperCamelCase__  )
    common(is_instance_map=UpperCamelCase__							,	segmentation_type='''pil'''  )
    common(is_instance_map=UpperCamelCase__							,	segmentation_type='''pil'''  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Tuple:
    _UpperCamelCase       			=np.zeros((20, 50)  )
    _UpperCamelCase       			=1
    _UpperCamelCase       			=1
    _UpperCamelCase       			=1

    _UpperCamelCase       			=binary_mask_to_rle(UpperCamelCase__  )
    self.assertEqual(len(UpperCamelCase__  )							,	4  )
    self.assertEqual(rle[0]							,	21  )
    self.assertEqual(rle[1]							,	45  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()

    _UpperCamelCase       			=fature_extractor.post_process_semantic_segmentation(UpperCamelCase__  )

    self.assertEqual(len(UpperCamelCase__  )							,	self.image_processing_tester.batch_size  )
    self.assertEqual(
        segmentation[0].shape							,	(
            self.image_processing_tester.height,
            self.image_processing_tester.width,
        )							,	)

    _UpperCamelCase       			=[(1, 4) for i in range(self.image_processing_tester.batch_size  )]
    _UpperCamelCase       			=fature_extractor.post_process_semantic_segmentation(UpperCamelCase__							,	target_sizes=UpperCamelCase__  )

    self.assertEqual(segmentation[0].shape							,	target_sizes[0]  )


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase       			=image_processor.post_process_instance_segmentation(UpperCamelCase__							,	threshold=0  )

    self.assertTrue(len(UpperCamelCase__  ) == self.image_processing_tester.batch_size  )
    for el in segmentation:
      self.assertTrue('''segmentation''' in el  )
      self.assertTrue('''segments_info''' in el  )
      self.assertEqual(type(el['''segments_info''']  )							,	UpperCamelCase__  )
      self.assertEqual(
          el['''segmentation'''].shape							,	(self.image_processing_tester.height, self.image_processing_tester.width)  )




  def   UpperCamelCase__      (				self    :					int  )     ->						Tuple:
    _UpperCamelCase       			=self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes							,	max_seq_length=77							,	task_seq_length=77							,	class_info_file='''ade20k_panoptic.json'''							,	num_text=self.image_processing_tester.num_text							,	repo_path='''shi-labs/oneformer_demo'''							,	)
    _UpperCamelCase       			=self.image_processing_tester.get_fake_oneformer_outputs()
    _UpperCamelCase       			=image_processor.post_process_panoptic_segmentation(UpperCamelCase__							,	threshold=0  )

    self.assertTrue(len(UpperCamelCase__  ) == self.image_processing_tester.batch_size  )
    for el in segmentation:
      self.assertTrue('''segmentation''' in el  )
      self.assertTrue('''segments_info''' in el  )
      self.assertEqual(type(el['''segments_info''']  )							,	UpperCamelCase__  )
      self.assertEqual(
          el['''segmentation'''].shape							,	(self.image_processing_tester.height, self.image_processing_tester.width)  )













'''simple docstring'''






import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

__lowerCamelCase						: List[Any]		=   'bert-base-cased'
__lowerCamelCase						: str		=   'fp16'
__lowerCamelCase						: Optional[int]		=   'bf16'
__lowerCamelCase						: List[Any]		=   [FPaa, BFaa]







@require_fsdp
@require_cuda
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Union[str, Any]:
    super().setUp()

    _UpperCamelCase       			=dict(
        ACCELERATE_USE_FSDP='''true'''							,	MASTER_ADDR='''localhost'''							,	MASTER_PORT='''10999'''							,	RANK='''0'''							,	LOCAL_RANK='''0'''							,	WORLD_SIZE='''1'''							,	)


  def   UpperCamelCase__      (				self    :					int  )     ->						List[Any]:
    from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

    for i, strategy in enumerate(UpperCamelCase__  ):
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=F'''{i + 1}'''
      _UpperCamelCase       			=strategy
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=FullyShardedDataParallelPlugin()
        self.assertEqual(fsdp_plugin.sharding_strategy							,	ShardingStrategy(i + 1  )  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Any:
    from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

    for i, prefetch_policy in enumerate(UpperCamelCase__  ):
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=prefetch_policy
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=FullyShardedDataParallelPlugin()
        if prefetch_policy == "NO_PREFETCH":
          self.assertIsNone(fsdp_plugin.backward_prefetch  )
        else:
          self.assertEqual(fsdp_plugin.backward_prefetch							,	BackwardPrefetch(i + 1  )  )


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Tuple:
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

    for i, state_dict_type in enumerate(UpperCamelCase__  ):
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=state_dict_type
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=FullyShardedDataParallelPlugin()
        self.assertEqual(fsdp_plugin.state_dict_type							,	StateDictType(i + 1  )  )
        if state_dict_type == "FULL_STATE_DICT":
          self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu  )
          self.assertTrue(fsdp_plugin.state_dict_config.ranka_only  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    _UpperCamelCase       			=AutoModel.from_pretrained(UpperCamelCase__  )
    for policy in FSDP_AUTO_WRAP_POLICY:
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=policy
      if policy == "TRANSFORMER_BASED_WRAP":
        _UpperCamelCase       			='''BertLayer'''
      elif policy == "SIZE_BASED_WRAP":
        _UpperCamelCase       			='''2000'''
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=FullyShardedDataParallelPlugin()
        fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__  )
        if policy == "NO_WRAP":
          self.assertIsNone(fsdp_plugin.auto_wrap_policy  )
        else:
          self.assertIsNotNone(fsdp_plugin.auto_wrap_policy  )

    _UpperCamelCase       			=self.dist_env.copy()
    _UpperCamelCase       			='''TRANSFORMER_BASED_WRAP'''
    _UpperCamelCase       			='''T5Layer'''
    with mockenv_context(**UpperCamelCase__  ):
      _UpperCamelCase       			=FullyShardedDataParallelPlugin()
      with self.assertRaises(UpperCamelCase__  ) as cm:
        fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__  )
      self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception  )  )

    _UpperCamelCase       			=self.dist_env.copy()
    _UpperCamelCase       			='''SIZE_BASED_WRAP'''
    _UpperCamelCase       			='''0'''
    with mockenv_context(**UpperCamelCase__  ):
      _UpperCamelCase       			=FullyShardedDataParallelPlugin()
      fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__  )
      self.assertIsNone(fsdp_plugin.auto_wrap_policy  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
    from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

    for mp_dtype in dtypes:
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=mp_dtype
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=Accelerator()
        if mp_dtype == "fp16":
          _UpperCamelCase       			=torch.floataa
        elif mp_dtype == "bf16":
          _UpperCamelCase       			=torch.bfloataa
        _UpperCamelCase       			=MixedPrecision(param_dtype=UpperCamelCase__							,	reduce_dtype=UpperCamelCase__							,	buffer_dtype=UpperCamelCase__  )
        self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy							,	UpperCamelCase__  )
        if mp_dtype == FPaa:
          self.assertTrue(isinstance(accelerator.scaler							,	UpperCamelCase__  )  )
        elif mp_dtype == BFaa:
          self.assertIsNone(accelerator.scaler  )
        AcceleratorState._reset_state(UpperCamelCase__  )




  def   UpperCamelCase__      (				self    :					Dict  )     ->						Tuple:
    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

    for flag in [True, False]:
      _UpperCamelCase       			=self.dist_env.copy()
      _UpperCamelCase       			=str(UpperCamelCase__  ).lower()
      with mockenv_context(**UpperCamelCase__  ):
        _UpperCamelCase       			=FullyShardedDataParallelPlugin()
        self.assertEqual(fsdp_plugin.cpu_offload							,	CPUOffload(offload_params=UpperCamelCase__  )  )







@require_fsdp
@require_multi_gpu
@slow
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    super().setUp()
    _UpperCamelCase       			=0.82
    _UpperCamelCase       			=[
        '''fsdp_shard_grad_op_transformer_based_wrap''',
        '''fsdp_full_shard_transformer_based_wrap''',
    ]
    _UpperCamelCase       			={
        '''multi_gpu_fp16''': 3200,
        '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2000,
        '''fsdp_full_shard_transformer_based_wrap_fp16''': 1900,
        # Disabling below test as it overwhelms the RAM memory usage
        # on CI self-hosted runner leading to tests getting killed.
        # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
    }
    _UpperCamelCase       			=160
    _UpperCamelCase       			=160

    _UpperCamelCase       			=inspect.getfile(accelerate.test_utils  )
    _UpperCamelCase       			=os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ['''scripts''', '''external_deps''']  )


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=os.path.join(self.test_scripts_folder							,	'''test_performance.py'''  )
    _UpperCamelCase       			=['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
    for config in self.performance_configs:
      _UpperCamelCase       			=cmd.copy()
      for i, strategy in enumerate(UpperCamelCase__  ):
        if strategy.lower() in config:
          cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''  )
          break

      if "fp32" in config:
        cmd_config.append('''--mixed_precision=no'''  )
      else:
        cmd_config.append('''--mixed_precision=fp16'''  )

      if "cpu_offload" in config:
        cmd_config.append('''--fsdp_offload_params=True'''  )

      for policy in FSDP_AUTO_WRAP_POLICY:
        if policy.lower() in config:
          cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''  )
          break

      if policy == "TRANSFORMER_BASED_WRAP":
        cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''  )
      elif policy == "SIZE_BASED_WRAP":
        cmd_config.append('''--fsdp_min_num_params=2000'''  )

      cmd_config.extend(
          [
              self.test_file_path,
              F'''--output_dir={self.tmpdir}''',
              F'''--performance_lower_bound={self.performance_lower_bound}''',
          ]  )
      with patch_environment(omp_num_threads=1  ):
        execute_subprocess_async(UpperCamelCase__							,	env=os.environ.copy()  )


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Any:
    _UpperCamelCase       			=os.path.join(self.test_scripts_folder							,	'''test_checkpointing.py'''  )
    _UpperCamelCase       			=[
        '''accelerate''',
        '''launch''',
        '''--num_processes=2''',
        '''--num_machines=1''',
        '''--machine_rank=0''',
        '''--use_fsdp''',
        '''--mixed_precision=fp16''',
        '''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
    ]

    for i, strategy in enumerate(UpperCamelCase__  ):
      _UpperCamelCase       			=cmd.copy()
      cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''  )
      if strategy != "FULL_SHARD":
        continue
      _UpperCamelCase       			=len(UpperCamelCase__  )
      for state_dict_type in FSDP_STATE_DICT_TYPE:
        _UpperCamelCase       			=cmd_config[:state_dict_config_index]
        cmd_config.append(F'''--fsdp_state_dict_type={state_dict_type}'''  )
        cmd_config.extend(
            [
                self.test_file_path,
                F'''--output_dir={self.tmpdir}''',
                '''--partial_train_epoch=1''',
            ]  )
        with patch_environment(omp_num_threads=1  ):
          execute_subprocess_async(UpperCamelCase__							,	env=os.environ.copy()  )

        _UpperCamelCase       			=cmd_config[:-1]
        _UpperCamelCase       			=os.path.join(self.tmpdir							,	'''epoch_0'''  )
        cmd_config.extend(
            [
                F'''--resume_from_checkpoint={resume_from_checkpoint}''',
            ]  )
        with patch_environment(omp_num_threads=1  ):
          execute_subprocess_async(UpperCamelCase__							,	env=os.environ.copy()  )




  def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
    _UpperCamelCase       			=os.path.join(self.test_scripts_folder							,	'''test_peak_memory_usage.py'''  )
    _UpperCamelCase       			=[
        '''accelerate''',
        '''launch''',
        '''--num_processes=2''',
        '''--num_machines=1''',
        '''--machine_rank=0''',
    ]
    for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
      _UpperCamelCase       			=cmd.copy()
      if "fp16" in spec:
        cmd_config.extend(['''--mixed_precision=fp16''']  )
      else:
        cmd_config.extend(['''--mixed_precision=no''']  )

      if "multi_gpu" in spec:
        continue
      else:
        cmd_config.extend(['''--use_fsdp''']  )
        for i, strategy in enumerate(UpperCamelCase__  ):
          if strategy.lower() in spec:
            cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''  )
            break

        if "cpu_offload" in spec:
          cmd_config.append('''--fsdp_offload_params=True'''  )

        for policy in FSDP_AUTO_WRAP_POLICY:
          if policy.lower() in spec:
            cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''  )
            break

        if policy == "TRANSFORMER_BASED_WRAP":
          cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''  )
        elif policy == "SIZE_BASED_WRAP":
          cmd_config.append('''--fsdp_min_num_params=2000'''  )

      cmd_config.extend(
          [
              self.test_file_path,
              F'''--output_dir={self.tmpdir}''',
              F'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
              F'''--n_train={self.n_train}''',
              F'''--n_val={self.n_val}''',
          ]  )
      with patch_environment(omp_num_threads=1  ):
        execute_subprocess_async(UpperCamelCase__							,	env=os.environ.copy()  )






