"""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=2		,      UpperCAmelCase=True		,      UpperCAmelCase=False      )       ->   Dict:
     snake_case_       =      Accelerator(step_scheduler_with_optimizer=UpperCAmelCase		,      split_batches=UpperCAmelCase      )
     snake_case_       =      torch.nn.Linear(2		,      4      )
     snake_case_       =      torch.optim.AdamW(model.parameters()		,      lr=1.0      )
     snake_case_       =      torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase		,      max_lr=0.01		,      steps_per_epoch=2		,      epochs=1      )
     snake_case_ ,			snake_case_ ,			snake_case_       =      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=2		,      UpperCAmelCase=True		,      UpperCAmelCase=False      )       ->   Dict:
     snake_case_       =      Accelerator(step_scheduler_with_optimizer=UpperCAmelCase		,      split_batches=UpperCAmelCase      )
     snake_case_       =      torch.nn.Linear(2		,      4      )
     snake_case_       =      torch.optim.AdamW(model.parameters()		,      lr=1.0      )
     snake_case_       =      torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase		,      lr_lambda=lambda UpperCAmelCase      : 1 - n / 10      )
     snake_case_ ,			snake_case_ ,			snake_case_       =      accelerator.prepare(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )

     # Optimizer has stepped
     snake_case_       =      False
     scheduler.step()
     snake_case_       =      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
     snake_case_       =      True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          snake_case_       =      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 = 2      )       ->   Tuple:
     from transformers import get_linear_schedule_with_warmup

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

          snake_case_ ,			snake_case_ ,			snake_case_       =      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 				UpperCamelCase      (       unittest.TestCase     ):







       def 	a_		(				self)   ->   str:
            debug_launcher(partial(lowerCAmelCase__,							num_processes=1),							num_processes=1)
            debug_launcher(partial(lowerCAmelCase__,							num_processes=1,							split_batches=lowerCAmelCase__),							num_processes=1)







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







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







       def 	a_		(				self)   ->   Optional[int]:
            debug_launcher(partial(lowerCAmelCase__,							num_processes=1,							step_scheduler_with_optimizer=lowerCAmelCase__),							num_processes=1)







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







       def 	a_		(				self)   ->   Any:
            AcceleratorState._reset_state(lowerCAmelCase__)
            debug_launcher(lowerCAmelCase__)
            debug_launcher(partial(lowerCAmelCase__,							num_processes=1,							split_batches=lowerCAmelCase__),							num_processes=1)







       def 	a_		(				self)   ->   int:
            AcceleratorState._reset_state(lowerCAmelCase__)
            debug_launcher(partial(lowerCAmelCase__,							step_scheduler_with_optimizer=lowerCAmelCase__))







       def 	a_		(				self)   ->   Tuple:
            AcceleratorState._reset_state(lowerCAmelCase__)
            debug_launcher(partial(lowerCAmelCase__,							step_scheduler_with_optimizer=lowerCAmelCase__))







       @require_huggingface_suite
       def 	a_		(				self)   ->   List[Any]:
            AcceleratorState._reset_state(lowerCAmelCase__)
            debug_launcher(partial(lowerCAmelCase__,							num_processes=1))
            debug_launcher(lowerCAmelCase__)


"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__=16,							lowerCAmelCase__=4,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   int:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      image_size // patch_size
            snake_case_       =      mask_input_channels
            snake_case_       =      num_point_embeddings
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__="relu",							lowerCAmelCase__=2048,							lowerCAmelCase__=2,							lowerCAmelCase__=8,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=256,							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      hidden_act
            snake_case_       =      mlp_dim
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      attention_downsample_rate
            snake_case_       =      num_multimask_outputs
            snake_case_       =      iou_head_depth
            snake_case_       =      iou_head_hidden_dim
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=768,							lowerCAmelCase__=256,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-06,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							lowerCAmelCase__=4.0,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=14,							lowerCAmelCase__=[2, 5, 8, 11],							lowerCAmelCase__=128,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)

            snake_case_       =      hidden_size
            snake_case_       =      output_channels
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      num_channels
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps
            snake_case_       =      attention_dropout
            snake_case_       =      initializer_range
            snake_case_       =      qkv_bias
            snake_case_       =      mlp_ratio
            snake_case_       =      use_abs_pos
            snake_case_       =      use_rel_pos
            snake_case_       =      window_size
            snake_case_       =      global_attn_indexes
            snake_case_       =      num_pos_feats
            snake_case_       =      int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "sam"
       SCREAMING_SNAKE_CASE_				=   True







       def __init__(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      vision_config if vision_config is not None else {}
            snake_case_       =      prompt_encoder_config if prompt_encoder_config is not None else {}
            snake_case_       =      mask_decoder_config if mask_decoder_config is not None else {}

            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      vision_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      prompt_encoder_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      mask_decoder_config.to_dict()

            snake_case_       =      SamVisionConfig(**lowerCAmelCase__)
            snake_case_       =      SamPromptEncoderConfig(**lowerCAmelCase__)
            snake_case_       =      SamMaskDecoderConfig(**lowerCAmelCase__)
            snake_case_       =      initializer_range







       def 	a_		(				self)   ->   Dict:
            snake_case_       =      copy.deepcopy(self.__dict__)
            snake_case_       =      self.vision_config.to_dict()
            snake_case_       =      self.prompt_encoder_config.to_dict()
            snake_case_       =      self.mask_decoder_config.to_dict()
            snake_case_       =      self.__class__.model_type
            return output

