


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()
UpperCamelCase								=	logging.get_logger()






@dataclass
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    42
       snake_case      =    field(default_factory=UpperCamelCase			)
       snake_case      =    field(default_factory=UpperCamelCase			)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





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



       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





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







       @property
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





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






@dataclass
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    42
       snake_case      =    42
       snake_case      =    1
       snake_case      =    field(default_factory=UpperCamelCase			)
       snake_case      =    field(default_factory=UpperCamelCase			)
       snake_case      =    True



       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					Tracker(self.dest		)(_SCREAMING_SNAKE_CASE		).parametrized
         A_      :				Tuple		     =					Tracker(self.src		)(_SCREAMING_SNAKE_CASE		).parametrized

         A_      :				Any		     =					list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.src_skip				,       _SCREAMING_SNAKE_CASE		)		)
         A_      :				List[Any]		     =					list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.dest_skip				,       _SCREAMING_SNAKE_CASE		)		)

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

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






class   _lowerCamelCase (					nn.Module			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





         super().__init__()

         A_      :				List[Tuple[str, nn.Module]]		     =					[]
         # - 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_      :				Tuple		     =					len(_SCREAMING_SNAKE_CASE		) + 1
           feature_blocks.append((F'''res{block_index}''', v)		)

         A_      :				str		     =					nn.ModuleDict(_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Any:




         '''simple docstring'''





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






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





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







       def __getitem__(      self				,       _SCREAMING_SNAKE_CASE		)->Callable[[], Tuple[nn.Module, Dict]]:




         '''simple docstring'''





         if x not in self:
           A_      :				List[str]		     =					self.convert_name_to_timm(_SCREAMING_SNAKE_CASE		)
           A_      :				Any		     =					partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE				,       pretrained=_SCREAMING_SNAKE_CASE		).eval(), None)		)

         else:
           A_      :				List[Any]		     =					super().__getitem__(_SCREAMING_SNAKE_CASE		)

         return val






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __getitem__(      self				,       _SCREAMING_SNAKE_CASE		)->Callable[[], nn.Module]:




         '''simple docstring'''





         if "seer" in x and "in1k" not in x:
           A_      :				Dict		     =					RegNetModel
         else:
           A_      :				Any		     =					RegNetForImageClassification
         return val




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  for from_key, to_key in keys:
    A_      :				List[Any]		     =					from_state_dict[from_key].clone()
    print(f'''Copied key={from_key} to={to_key}'''       )
  return to_state_dict


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = True ,			):
  print(f'''Converting {name}...'''       )
  with torch.no_grad():
    A_     ,       A_      :				List[str]		     =					from_model_func()
    A_      :				int		     =					our_model_func(SCREAMING_SNAKE_CASE       ).eval()
    A_      :				str		     =					ModuleTransfer(src=SCREAMING_SNAKE_CASE ,			dest=SCREAMING_SNAKE_CASE ,			raise_if_mismatch=SCREAMING_SNAKE_CASE       )
    A_      :				Union[str, Any]		     =					torch.randn((1, 3, 224, 224)       )
    module_transfer(SCREAMING_SNAKE_CASE       )

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

  A_      :				Dict		     =					our_model(SCREAMING_SNAKE_CASE ,			output_hidden_states=SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					(
      our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else our_outputs.last_hidden_state
  )

  A_      :				int		     =					from_model(SCREAMING_SNAKE_CASE       )
  A_      :				List[str]		     =					from_output[-1] if type(SCREAMING_SNAKE_CASE       ) 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_      :				List[Any]		     =					our_outputs.hidden_states[-1]

  assert torch.allclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ), "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=SCREAMING_SNAKE_CASE ,			)

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

    print(f'''Pushed {name}'''       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE = True       ):
  A_      :				Union[str, Any]		     =					'''imagenet-1k-id2label.json'''
  A_      :				Union[str, Any]		     =					1_000
  A_      :				List[str]		     =					(1, num_labels)

  A_      :				List[Any]		     =					'''huggingface/label-files'''
  A_      :				Union[str, Any]		     =					num_labels
  A_      :				Tuple		     =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			repo_type='''dataset'''       )       ) ,			'''r'''       )       )
  A_      :				int		     =					{int(SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}

  A_      :				str		     =					idalabel
  A_      :				List[str]		     =					{v: k for k, v in idalabel.items()}

  A_      :				Union[str, Any]		     =					partial(SCREAMING_SNAKE_CASE ,			num_labels=SCREAMING_SNAKE_CASE ,			idalabel=SCREAMING_SNAKE_CASE ,			labelaid=SCREAMING_SNAKE_CASE       )

  A_      :				str		     =					{
      '''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, 1_008] ,			groups_width=48 ,			layer_type='''x'''       ),
      '''regnet-x-040''': ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2] ,			hidden_sizes=[80, 240, 560, 1_360] ,			groups_width=40 ,			layer_type='''x'''       ),
      '''regnet-x-064''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1] ,			hidden_sizes=[168, 392, 784, 1_624] ,			groups_width=56 ,			layer_type='''x'''       ),
      '''regnet-x-080''': ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1] ,			hidden_sizes=[80, 240, 720, 1_920] ,			groups_width=120 ,			layer_type='''x'''       ),
      '''regnet-x-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1] ,			hidden_sizes=[224, 448, 896, 2_240] ,			groups_width=112 ,			layer_type='''x'''       ),
      '''regnet-x-160''': ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1] ,			hidden_sizes=[256, 512, 896, 2_048] ,			groups_width=128 ,			layer_type='''x'''       ),
      '''regnet-x-320''': ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1] ,			hidden_sizes=[336, 672, 1_344, 2_520] ,			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, 1_512] ,			groups_width=24       ),
      '''regnet-y-040''': ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2] ,			hidden_sizes=[128, 192, 512, 1_088] ,			groups_width=64       ),
      '''regnet-y-064''': ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2] ,			hidden_sizes=[144, 288, 576, 1_296] ,			groups_width=72       ),
      '''regnet-y-080''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1] ,			hidden_sizes=[168, 448, 896, 2_016] ,			groups_width=56       ),
      '''regnet-y-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1] ,			hidden_sizes=[224, 448, 896, 2_240] ,			groups_width=112       ),
      '''regnet-y-160''': ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1] ,			hidden_sizes=[224, 448, 1_232, 3_024] ,			groups_width=112       ),
      '''regnet-y-320''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,			hidden_sizes=[232, 696, 1_392, 3_712] ,			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, 1_392, 3_712] ,			groups_width=232       ),
      '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1] ,			hidden_sizes=[328, 984, 1_968, 4_920] ,			groups_width=328       ),
      '''regnet-y-1280-seer''': RegNetConfig(
          depths=[2, 7, 17, 1] ,			hidden_sizes=[528, 1_056, 2_904, 7_392] ,			groups_width=264       ),
      '''regnet-y-2560-seer''': RegNetConfig(
          depths=[3, 7, 16, 1] ,			hidden_sizes=[640, 1_696, 2_544, 5_088] ,			groups_width=640       ),
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280] ,			groups_width=1_010       ),
      # finetuned on imagenet
      '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,			hidden_sizes=[232, 696, 1_392, 3_712] ,			groups_width=232       ),
      '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1] ,			hidden_sizes=[328, 984, 1_968, 4_920] ,			groups_width=328       ),
      '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,			hidden_sizes=[528, 1_056, 2_904, 7_392] ,			groups_width=264       ),
      '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1] ,			hidden_sizes=[640, 1_696, 2_544, 5_088] ,			groups_width=640       ),
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280] ,			groups_width=1_010       ),
  }

  A_      :				Dict		     =					NameToOurModelFuncMap()
  A_      :				Optional[Any]		     =					NameToFromModelFuncMap()
  # add seer weights logic

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

  # pretrained
  A_      :				List[Any]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,			)

  A_      :				List[Any]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,			)

  A_      :				Tuple		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''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_      :				Optional[Any]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' ,			lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27 ,			group_width=1_010 ,			w_a=1_744 ,			w_a=6_2_0.8_3 ,			w_m=2.5_2       )       )       ) ,			)

  # IN1K finetuned
  A_      :				List[str]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,			)

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

  A_      :				Union[str, Any]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()       ) ,			)

  A_      :				Optional[int]		     =					partial(
      SCREAMING_SNAKE_CASE ,			'''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=1_010 ,			w_a=1_744 ,			w_a=6_2_0.8_3 ,			w_m=2.5_2       )       )       ) ,			)

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


if __name__ == "__main__":
     UpperCamelCase								=	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.""",
     )

     UpperCamelCase								=	parser.parse_args()

     UpperCamelCase								=	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)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1 / 255				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					size if size is not None else {'''shortest_edge''': 224}
         A_      :				Any		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''		)

         A_      :				str		     =					do_resize
         A_      :				Tuple		     =					size
         A_      :				Optional[Any]		     =					resample
         A_      :				Tuple		     =					do_center_crop
         A_      :				List[Any]		     =					crop_size
         A_      :				Optional[int]		     =					do_rescale
         A_      :				Tuple		     =					rescale_factor
         A_      :				Any		     =					do_normalize
         A_      :				int		     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_      :				Any		     =					image_std if image_std is not None else OPENAI_CLIP_STD
         A_      :				Any		     =					do_convert_rgb



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
         A_      :				Any		     =					get_resize_output_image_size(_SCREAMING_SNAKE_CASE				,       size=size['''shortest_edge''']				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         return resize(_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				str		     =					get_size_dict(_SCREAMING_SNAKE_CASE		)
         if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
         return center_crop(_SCREAMING_SNAKE_CASE				,       size=(size['''height'''], size['''width'''])				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





         return rescale(_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         return normalize(_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,       **_SCREAMING_SNAKE_CASE				,       )->PIL.Image.Image:




         '''simple docstring'''





         A_      :				Optional[int]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				int		     =					size if size is not None else self.size
         A_      :				Optional[int]		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					resample if resample is not None else self.resample
         A_      :				Any		     =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A_      :				List[str]		     =					crop_size if crop_size is not None else self.crop_size
         A_      :				int		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				int		     =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A_      :				int		     =					do_normalize if do_normalize is not None else self.do_normalize
         A_      :				Tuple		     =					image_mean if image_mean is not None else self.image_mean
         A_      :				Tuple		     =					image_std if image_std is not None else self.image_std
         A_      :				List[str]		     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_      :				Optional[int]		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

         if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''		)

         if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

         if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           A_      :				List[str]		     =					[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

         # All transformations expect numpy arrays.
         A_      :				int		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

         if do_resize:
           A_      :				Tuple		     =					[self.resize(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_center_crop:
           A_      :				Union[str, Any]		     =					[self.center_crop(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				Tuple		     =					[self.rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_normalize:
           A_      :				List[Any]		     =					[self.normalize(image=_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				str		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Optional[Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)




