




"""simple docstring"""






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)




class   a__			(       nn.Module  ):





      def __init__(							self      :	Any)						->       str:




           """simple docstring"""







           super().__init__()
           _lowerCAmelCase:int									=  nn.Linear(3     ,4)
           _lowerCAmelCase:List[str]									=  nn.BatchNormad(4)
           _lowerCAmelCase:Optional[int]									=  nn.Linear(4     ,5)







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




           """simple docstring"""







           return self.lineara(self.batchnorm(self.lineara(a__)))




class   a__			(       unittest.TestCase  ):





      def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




           """simple docstring"""







           _lowerCAmelCase:List[Any]									=  ModelForTest()
           with TemporaryDirectory() as tmp_dir:
                offload_state_dict(a__     ,model.state_dict())
                _lowerCAmelCase:List[str]									=  os.path.join(a__     ,'''index.json''')
                self.assertTrue(os.path.isfile(a__))
                # TODO: add tests on what is inside the index

                for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                     _lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,F'{key}.dat')
                     self.assertTrue(os.path.isfile(a__))
                     # TODO: add tests on the fact weights are properly loaded





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




           """simple docstring"""







           _lowerCAmelCase:List[Any]									=  [torch.floataa, torch.floataa, torch.bfloataa]

           for dtype in dtypes:
                _lowerCAmelCase:Optional[Any]									=  torch.randn(2     ,3     ,dtype=a__)
                with TemporaryDirectory() as tmp_dir:
                     _lowerCAmelCase:int									=  offload_weight(a__     ,'''weight'''     ,a__     ,{})
                     _lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''weight.dat''')
                     self.assertTrue(os.path.isfile(a__))
                     self.assertDictEqual(a__     ,{'''weight''': {'''shape''': [2, 3], '''dtype''': str(a__).split('''.''')[1]}})

                     _lowerCAmelCase:int									=  load_offloaded_weight(a__     ,index['''weight'''])
                     self.assertTrue(torch.equal(a__     ,a__))





      def       __UpperCamelCase     (							self      :	Optional[int])						->       Optional[int]:




           """simple docstring"""







           _lowerCAmelCase:List[str]									=  ModelForTest()
           _lowerCAmelCase:Dict									=  model.state_dict()
           _lowerCAmelCase:Tuple									=  {k: v for k, v in state_dict.items() if '''linear2''' not in k}
           _lowerCAmelCase:List[str]									=  {k: v for k, v in state_dict.items() if '''linear2''' in k}

           with TemporaryDirectory() as tmp_dir:
                offload_state_dict(a__     ,a__)
                _lowerCAmelCase:int									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

                # Every key is there with the right value
                self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
                for key, param in state_dict.items():
                     self.assertTrue(torch.allclose(a__     ,weight_map[key]))

           _lowerCAmelCase:Any									=  {k: v for k, v in state_dict.items() if '''weight''' in k}
           _lowerCAmelCase:str									=  {k: v for k, v in state_dict.items() if '''weight''' not in k}

           with TemporaryDirectory() as tmp_dir:
                offload_state_dict(a__     ,a__)
                _lowerCAmelCase:Any									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

                # Every key is there with the right value
                self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
                for key, param in state_dict.items():
                     self.assertTrue(torch.allclose(a__     ,weight_map[key]))

           with TemporaryDirectory() as tmp_dir:
                offload_state_dict(a__     ,a__)
                # Duplicates are removed
                _lowerCAmelCase:Dict									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

                # Every key is there with the right value
                self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
                for key, param in state_dict.items():
                     self.assertTrue(torch.allclose(a__     ,weight_map[key]))







      def       __UpperCamelCase     (							self      :	List[str])						->       str:




           """simple docstring"""







           _lowerCAmelCase:Union[str, Any]									=  {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
           _lowerCAmelCase:str									=  extract_submodules_state_dict(a__     ,['''a.1''', '''a.2'''])
           self.assertDictEqual(a__     ,{'''a.1''': 0, '''a.2''': 2})

           _lowerCAmelCase:Union[str, Any]									=  {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
           _lowerCAmelCase:str									=  extract_submodules_state_dict(a__     ,['''a.1''', '''a.2'''])
           self.assertDictEqual(a__     ,{'''a.1.a''': 0, '''a.2.a''': 2})












"""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






