




"""simple docstring"""






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        Adafactor,
        AdamW,
        get_constant_schedule,
        get_constant_schedule_with_warmup,
        get_cosine_schedule_with_warmup,
        get_cosine_with_hard_restarts_schedule_with_warmup,
        get_inverse_sqrt_schedule,
        get_linear_schedule_with_warmup,
        get_polynomial_decay_schedule_with_warmup,
    )





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]      ,       snake_case			:					Union[str, Any]=10    ):
     _lowerCAmelCase:Dict									=  []
     for _ in range(snake_case__    ):
          lrs.append(scheduler.get_lr()[0]    )
          scheduler.step()
     return lrs





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					Dict=10    ):
     _lowerCAmelCase:List[str]									=  []
     for step in range(snake_case__    ):
          lrs.append(scheduler.get_lr()[0]    )
          scheduler.step()
          if step == num_steps // 2:
               with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCAmelCase:Union[str, Any]									=  os.path.join(snake_case__      ,       '''schedule.bin'''    )
                    torch.save(scheduler.state_dict()      ,       snake_case__    )

                    _lowerCAmelCase:Union[str, Any]									=  torch.load(snake_case__    )
                    scheduler.load_state_dict(snake_case__    )
     return lrs




@require_torch
class   a__			(       unittest.TestCase  ):





      def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	str     ,a__      :	Tuple     ,a__      :	List[Any])						->       List[str]:




           """simple docstring"""







           self.assertEqual(len(A_)     ,len(A_))
           for a, b in zip(A_     ,A_):
                self.assertAlmostEqual(A_     ,A_     ,delta=A_)





      def       __UpperCamelCase     (							self      :	Optional[Any])						->       Dict:




           """simple docstring"""







           _lowerCAmelCase:Union[str, Any]									=  torch.tensor([0.1, -0.2, -0.1]     ,requires_grad=A_)
           _lowerCAmelCase:str									=  torch.tensor([0.4, 0.2, -0.5])
           _lowerCAmelCase:int									=  nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           _lowerCAmelCase:Any									=  AdamW(params=[w]     ,lr=2E-1     ,weight_decay=0.0)
           for _ in range(100):
                _lowerCAmelCase:Any									=  criterion(A_     ,A_)
                loss.backward()
                optimizer.step()
                w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()     ,[0.4, 0.2, -0.5]     ,tol=1E-2)







      def       __UpperCamelCase     (							self      :	str)						->       Optional[Any]:




           """simple docstring"""







           _lowerCAmelCase:int									=  torch.tensor([0.1, -0.2, -0.1]     ,requires_grad=A_)
           _lowerCAmelCase:int									=  torch.tensor([0.4, 0.2, -0.5])
           _lowerCAmelCase:str									=  nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           _lowerCAmelCase:List[Any]									=  Adafactor(
               params=[w]     ,lr=1E-2     ,eps=(1E-30, 1E-3)     ,clip_threshold=1.0     ,decay_rate=-0.8     ,betaa=A_     ,weight_decay=0.0     ,relative_step=A_     ,scale_parameter=A_     ,warmup_init=A_     ,)
           for _ in range(1000):
                _lowerCAmelCase:Union[str, Any]									=  criterion(A_     ,A_)
                loss.backward()
                optimizer.step()
                w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()     ,[0.4, 0.2, -0.5]     ,tol=1E-2)




@require_torch
class   a__			(       unittest.TestCase  ):
      snake_case__             = nn.Linear(5_0					,	5_0  ) if is_torch_available() else None
      snake_case__             = AdamW(m.parameters()					,	lr=10.0  ) if is_torch_available() else None
      snake_case__             = 1_0





      def       __UpperCamelCase     (							self      :	Any     ,a__      :	List[Any]     ,a__      :	Dict     ,a__      :	Dict     ,a__      :	List[Any]=None)						->       int:




           """simple docstring"""







           self.assertEqual(len(A_)     ,len(A_))
           for a, b in zip(A_     ,A_):
                self.assertAlmostEqual(A_     ,A_     ,delta=A_     ,msg=A_)







      def       __UpperCamelCase     (							self      :	List[Any])						->       Tuple:




           """simple docstring"""







           _lowerCAmelCase:List[Any]									=  {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           _lowerCAmelCase:Any									=  {
               get_constant_schedule: ({}, [10.0] * self.num_steps),
               get_constant_schedule_with_warmup: (
                   {'''num_warmup_steps''': 4},
                   [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
               ),
               get_linear_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
               ),
               get_cosine_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
               ),
               get_cosine_with_hard_restarts_schedule_with_warmup: (
                   {**common_kwargs, '''num_cycles''': 2},
                   [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
               ),
               get_polynomial_decay_schedule_with_warmup: (
                   {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
                   [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
               ),
               get_inverse_sqrt_schedule: (
                   {'''num_warmup_steps''': 2},
                   [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
               ),
           }

           for scheduler_func, data in scheds.items():
                _lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  data

                _lowerCAmelCase:Tuple									=  scheduler_func(self.optimizer     ,**A_)
                self.assertEqual(len([scheduler.get_lr()[0]])     ,1)
                _lowerCAmelCase:int									=  unwrap_schedule(A_     ,self.num_steps)
                self.assertListAlmostEqual(
                    A_     ,A_     ,tol=1E-2     ,msg=F'failed for {scheduler_func} in normal scheduler'     ,)

                _lowerCAmelCase:Dict									=  scheduler_func(self.optimizer     ,**A_)
                if scheduler_func.__name__ != "get_constant_schedule":
                     LambdaScheduleWrapper.wrap_scheduler(A_)  # wrap to test picklability of the schedule
                _lowerCAmelCase:str									=  unwrap_and_save_reload_schedule(A_     ,self.num_steps)
                self.assertListEqual(A_     ,A_     ,msg=F'failed for {scheduler_func} in save and reload')




class   a__			:





      def __init__(							self      :	Tuple     ,a__      :	Dict)						->       Optional[int]:




           """simple docstring"""







           _lowerCAmelCase:List[Any]									=  fn





      def __call__(							self      :	Tuple     ,*a__      :	List[Any]     ,**a__      :	str)						->       Optional[Any]:




           """simple docstring"""







           return self.fn(*A_     ,**A_)







      @classmethod
      def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	Optional[Any])						->       int:




           """simple docstring"""







           _lowerCAmelCase:List[str]									=  list(map(self     ,scheduler.lr_lambdas))












"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCamelCase__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,*a__      :	Optional[Any]     ,**a__      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											super().__init__(*a__     ,**a__)
											requires_backends(self     ,'''vision''')
											self.check_model_type(a__)





						def __call__(							self      :	str     ,a__      :	Union[str, List[str], "Image.Image", List["Image.Image"]]     ,**a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											return super().__call__(a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,**a__      :	List[Any])						->       Any:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  load_image(a__)
											_lowerCAmelCase:int									=  image.size
											_lowerCAmelCase:int									=  self.image_processor(images=a__     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model(**a__)
											return model_outputs







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Dict)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  model_outputs.predicted_depth
											_lowerCAmelCase:Union[str, Any]									=  torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1)     ,size=self.image_size[::-1]     ,mode='''bicubic'''     ,align_corners=a__)
											_lowerCAmelCase:List[str]									=  prediction.squeeze().cpu().numpy()
											_lowerCAmelCase:Any									=  (output * 255 / np.max(a__)).astype('''uint8''')
											_lowerCAmelCase:Dict									=  Image.fromarray(a__)
											_lowerCAmelCase:Tuple									=  {}
											_lowerCAmelCase:Optional[int]									=  predicted_depth
											_lowerCAmelCase:str									=  depth
											return output_dict






