

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_			=  logging.get_logger()


@dataclass
class 			A						:
    lowerCamelCase		     =							42
    lowerCamelCase		     =							field(default_factory=lowerCamelCase__			)
    lowerCamelCase		     =							field(default_factory=lowerCamelCase__			)


    def 						snake_case__					(				self			:	str,lowercase_			:	Tuple,lowercase_			:	Tensor,lowercase_			:	Tensor		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       len(list(m.modules()		)		) == 1 or isinstance(lowercase__,nn.Convad		) or isinstance(lowercase__,nn.BatchNormad		)
        if has_not_submodules:
            self.traced.append(lowercase__		)


    def __call__(				self			:	Tuple,lowercase_			:	Tensor		)->   Union[str, Any]:





        '''simple docstring'''







        for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook		)		)
        self.module(lowercase__		)
        [x.remove() for x in self.handles]
        return self


    @property
    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        return list(filter(lambda lowercase_		: len(list(x.state_dict().keys()		)		) > 0,self.traced		)		)




@dataclass
class 			A						:
    lowerCamelCase		     =							42
    lowerCamelCase		     =							42
    lowerCamelCase		     =							1
    lowerCamelCase		     =							field(default_factory=lowerCamelCase__			)
    lowerCamelCase		     =							field(default_factory=lowerCamelCase__			)
    lowerCamelCase		     =							True


    def __call__(				self			:	int,lowercase_			:	Tensor		)->   List[str]:





        '''simple docstring'''







        A__   				=       Tracker(self.dest		)(lowercase__		).parametrized
        A__   				=       Tracker(self.src		)(lowercase__		).parametrized

        A__   				=       list(filter(lambda lowercase_		: type(lowercase__		) not in self.src_skip,lowercase__		)		)
        A__   				=       list(filter(lambda lowercase_		: type(lowercase__		) not in self.dest_skip,lowercase__		)		)

        if len(lowercase__		) != len(lowercase__		) and self.raise_if_mismatch:
            raise Exception(
                F'Numbers of operations are different. Source module has {len(lowercase__		)} operations while'
                F' destination module has {len(lowercase__		)}.'		)

        for dest_m, src_m in zip(lowercase__,lowercase__		):
            dest_m.load_state_dict(src_m.state_dict()		)
            if self.verbose == 1:
                print(F'Transfered from={src_m} to={dest_m}'		)




class 			A						(							nn.Module			):


    def __init__(				self			:	Tuple,lowercase_			:	nn.Module		)->   List[Any]:





        '''simple docstring'''







        super().__init__()

        A__   				=       []
        # - get the stem
        feature_blocks.append(('conv1', model.stem)		)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
            assert k.startswith('block'		), F'Unexpected layer name {k}'
            A__   				=       len(lowercase__		) + 1
            feature_blocks.append((F'res{block_index}', v)		)

        A__   				=       nn.ModuleDict(lowercase__		)


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Tensor		)->   Optional[int]:





        '''simple docstring'''







        return get_trunk_forward_outputs(
            lowercase__,out_feat_keys=lowercase__,feature_blocks=self._feature_blocks,)




class 			A						(							lowerCamelCase__			):


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	str		)->   Tuple:





        '''simple docstring'''







        A__   				=       x.split('-'		)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)


    def __getitem__(				self			:	Tuple,lowercase_			:	str		)->   Any:





        '''simple docstring'''







        if x not in self:
            A__   				=       self.convert_name_to_timm(lowercase__		)
            A__   				=       partial(lambda: (timm.create_model(lowercase__,pretrained=lowercase__		).eval(), None)		)

        else:
            A__   				=       super().__getitem__(lowercase__		)

        return val




class 			A						(							lowerCamelCase__			):


    def __getitem__(				self			:	int,lowercase_			:	str		)->   Union[str, Any]:





        '''simple docstring'''







        if "seer" in x and "in1k" not in x:
            A__   				=       RegNetModel
        else:
            A__   				=       RegNetForImageClassification
        return val




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				Tuple:





    '''simple docstring'''






    for from_key, to_key in keys:
        A__   				=       from_state_dict[from_key].clone()
        print(f'Copied key={from_key} to={to_key}'				)
    return to_state_dict
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any] = True   ,      )							->				Optional[Any]:





    '''simple docstring'''






    print(f'Converting {name}...'				)
    with torch.no_grad():
        A__				,						A__   				=       from_model_func()
        A__   				=       our_model_func(A__				).eval()
        A__   				=       ModuleTransfer(src=A__   ,      dest=A__   ,      raise_if_mismatch=A__				)
        A__   				=       torch.randn((1, 3, 224, 224)				)
        module_transfer(A__				)

    if from_state_dict is not None:
        A__   				=       []
        # for seer - in1k finetuned we have to manually copy the head
        if "seer" in name and "in1k" in name:
            A__   				=       [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
        A__   				=       manually_copy_vissl_head(A__   ,      our_model.state_dict()   ,      A__				)
        our_model.load_state_dict(A__				)

    A__   				=       our_model(A__   ,      output_hidden_states=A__				)
    A__   				=       (
        our_outputs.logits if isinstance(A__   ,      A__				) else our_outputs.last_hidden_state
    )

    A__   				=       from_model(A__				)
    A__   				=       from_output[-1] if type(A__				) is list else from_output

    # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
    if "seer" in name and "in1k" in name:
        A__   				=       our_outputs.hidden_states[-1]

    assert torch.allclose(A__   ,      A__				), "The model logits don't match the original one."

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / name   ,      commit_message='Add model'   ,      use_temp_dir=A__   ,      )

        A__   				=       224 if 'seer' not in name else 384
        # we can use the convnext one
        A__   				=       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'   ,      size=A__				)
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / name   ,      commit_message='Add image processor'   ,      use_temp_dir=A__   ,      )

        print(f'Pushed {name}'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any] = None   ,      SCREAMING_SNAKE_CASE__      :     List[str] = True				)							->				Dict:





    '''simple docstring'''






    A__   				=       'imagenet-1k-id2label.json'
    A__   				=       1000
    A__   				=       (1, num_labels)

    A__   				=       'huggingface/label-files'
    A__   				=       num_labels
    A__   				=       json.load(open(cached_download(hf_hub_url(A__   ,      A__   ,      repo_type='dataset'				)				)   ,      'r'				)				)
    A__   				=       {int(A__				): v for k, v in idalabel.items()}

    A__   				=       idalabel
    A__   				=       {v: k for k, v in idalabel.items()}

    A__   				=       partial(A__   ,      num_labels=A__   ,      idalabel=A__   ,      labelaid=A__				)

    A__   				=       {
        'regnet-x-002': ImageNetPreTrainedConfig(
            depths=[1, 1, 4, 7]   ,      hidden_sizes=[24, 56, 152, 368]   ,      groups_width=8   ,      layer_type='x'				),
        'regnet-x-004': ImageNetPreTrainedConfig(
            depths=[1, 2, 7, 12]   ,      hidden_sizes=[32, 64, 160, 384]   ,      groups_width=16   ,      layer_type='x'				),
        'regnet-x-006': ImageNetPreTrainedConfig(
            depths=[1, 3, 5, 7]   ,      hidden_sizes=[48, 96, 240, 528]   ,      groups_width=24   ,      layer_type='x'				),
        'regnet-x-008': ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 5]   ,      hidden_sizes=[64, 128, 288, 672]   ,      groups_width=16   ,      layer_type='x'				),
        'regnet-x-016': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 2]   ,      hidden_sizes=[72, 168, 408, 912]   ,      groups_width=24   ,      layer_type='x'				),
        'regnet-x-032': ImageNetPreTrainedConfig(
            depths=[2, 6, 15, 2]   ,      hidden_sizes=[96, 192, 432, 1008]   ,      groups_width=48   ,      layer_type='x'				),
        'regnet-x-040': ImageNetPreTrainedConfig(
            depths=[2, 5, 14, 2]   ,      hidden_sizes=[80, 240, 560, 1360]   ,      groups_width=40   ,      layer_type='x'				),
        'regnet-x-064': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]   ,      hidden_sizes=[168, 392, 784, 1624]   ,      groups_width=56   ,      layer_type='x'				),
        'regnet-x-080': ImageNetPreTrainedConfig(
            depths=[2, 5, 15, 1]   ,      hidden_sizes=[80, 240, 720, 1920]   ,      groups_width=120   ,      layer_type='x'				),
        'regnet-x-120': ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]   ,      hidden_sizes=[224, 448, 896, 2240]   ,      groups_width=112   ,      layer_type='x'				),
        'regnet-x-160': ImageNetPreTrainedConfig(
            depths=[2, 6, 13, 1]   ,      hidden_sizes=[256, 512, 896, 2048]   ,      groups_width=128   ,      layer_type='x'				),
        'regnet-x-320': ImageNetPreTrainedConfig(
            depths=[2, 7, 13, 1]   ,      hidden_sizes=[336, 672, 1344, 2520]   ,      groups_width=168   ,      layer_type='x'				),
        # y variant
        'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]   ,      hidden_sizes=[24, 56, 152, 368]   ,      groups_width=8				),
        'regnet-y-004': ImageNetPreTrainedConfig(
            depths=[1, 3, 6, 6]   ,      hidden_sizes=[48, 104, 208, 440]   ,      groups_width=8				),
        'regnet-y-006': ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 4]   ,      hidden_sizes=[48, 112, 256, 608]   ,      groups_width=16				),
        'regnet-y-008': ImageNetPreTrainedConfig(
            depths=[1, 3, 8, 2]   ,      hidden_sizes=[64, 128, 320, 768]   ,      groups_width=16				),
        'regnet-y-016': ImageNetPreTrainedConfig(
            depths=[2, 6, 17, 2]   ,      hidden_sizes=[48, 120, 336, 888]   ,      groups_width=24				),
        'regnet-y-032': ImageNetPreTrainedConfig(
            depths=[2, 5, 13, 1]   ,      hidden_sizes=[72, 216, 576, 1512]   ,      groups_width=24				),
        'regnet-y-040': ImageNetPreTrainedConfig(
            depths=[2, 6, 12, 2]   ,      hidden_sizes=[128, 192, 512, 1088]   ,      groups_width=64				),
        'regnet-y-064': ImageNetPreTrainedConfig(
            depths=[2, 7, 14, 2]   ,      hidden_sizes=[144, 288, 576, 1296]   ,      groups_width=72				),
        'regnet-y-080': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]   ,      hidden_sizes=[168, 448, 896, 2016]   ,      groups_width=56				),
        'regnet-y-120': ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]   ,      hidden_sizes=[224, 448, 896, 2240]   ,      groups_width=112				),
        'regnet-y-160': ImageNetPreTrainedConfig(
            depths=[2, 4, 11, 1]   ,      hidden_sizes=[224, 448, 1232, 3024]   ,      groups_width=112				),
        'regnet-y-320': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]   ,      hidden_sizes=[232, 696, 1392, 3712]   ,      groups_width=232				),
        # models created by SEER -> https://arxiv.org/abs/2202.08360
        'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]   ,      hidden_sizes=[232, 696, 1392, 3712]   ,      groups_width=232				),
        'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]   ,      hidden_sizes=[328, 984, 1968, 4920]   ,      groups_width=328				),
        'regnet-y-1280-seer': RegNetConfig(
            depths=[2, 7, 17, 1]   ,      hidden_sizes=[528, 1056, 2904, 7392]   ,      groups_width=264				),
        'regnet-y-2560-seer': RegNetConfig(
            depths=[3, 7, 16, 1]   ,      hidden_sizes=[640, 1696, 2544, 5088]   ,      groups_width=640				),
        'regnet-y-10b-seer': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]   ,      hidden_sizes=[2020, 4040, 11110, 28280]   ,      groups_width=1010				),
        # finetuned on imagenet
        'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]   ,      hidden_sizes=[232, 696, 1392, 3712]   ,      groups_width=232				),
        'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]   ,      hidden_sizes=[328, 984, 1968, 4920]   ,      groups_width=328				),
        'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]   ,      hidden_sizes=[528, 1056, 2904, 7392]   ,      groups_width=264				),
        'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
            depths=[3, 7, 16, 1]   ,      hidden_sizes=[640, 1696, 2544, 5088]   ,      groups_width=640				),
        'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]   ,      hidden_sizes=[2020, 4040, 11110, 28280]   ,      groups_width=1010				),
    }

    A__   				=       NameToOurModelFuncMap()
    A__   				=       NameToFromModelFuncMap()
    # add seer weights logic

    def load_using_classy_vision(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]				) -> Tuple[nn.Module, Dict]:
        A__   				=       torch.hub.load_state_dict_from_url(A__   ,      model_dir=str(A__				)   ,      map_location='cpu'				)
        A__   				=       model_func()
        # check if we have a head, if yes add it
        A__   				=       files['classy_state_dict']['base_model']['model']
        A__   				=       model_state_dict['trunk']
        model.load_state_dict(A__				)
        return model.eval(), model_state_dict["heads"]

    # pretrained
    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'   ,      lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27   ,      group_width=1010   ,      w_a=1744   ,      w_a=620.83   ,      w_m=2.52				)				)				)   ,      )

    # IN1K finetuned
    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)   ,      )

    A__   				=       partial(
        A__   ,      'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'   ,      lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27   ,      group_width=1010   ,      w_a=1744   ,      w_a=620.83   ,      w_m=2.52				)				)				)   ,      )

    if model_name:
        convert_weight_and_push(
            A__   ,      names_to_from_model_map[model_name]   ,      names_to_ours_model_map[model_name]   ,      names_to_config[model_name]   ,      A__   ,      A__   ,      )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(
                A__   ,      names_to_from_model_map[model_name]   ,      names_to_ours_model_map[model_name]   ,      A__   ,      A__   ,      A__   ,      )
    return config, expected_shape


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default=None,
         type=str,
         help=(
             "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
             " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
         ),
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=Path,
         required=True,
         help="Path to the output PyTorch model directory.",
     )
     parser.add_argument(
         "--push_to_hub",
         default=True,
         type=bool,
         required=False,
         help="If True, push model and image processor to the hub.",
     )

     lowercase_			=  parser.parse_args()

     lowercase_			=  args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

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 PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

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

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


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





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


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





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

