from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)



def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 return [
     int(1000 * (box[0] / width)       ),
     int(1000 * (box[1] / height)       ),
     int(1000 * (box[2] / width)       ),
     int(1000 * (box[3] / height)       ),
 ]



def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	to_pil_image(_A       )
 __a							,  __a							=	pil_image.size
 __a							=	pytesseract.image_to_data(_A				,					lang=_A				,					output_type="dict"				,					config=_A       )
 __a							,  __a							,  __a							,  __a							,  __a							=	data["text"], data["left"], data["top"], data["width"], data["height"]

 # filter empty words and corresponding coordinates
 __a							=	[idx for idx, word in enumerate(_A       ) if not word.strip()]
 __a							=	[word for idx, word in enumerate(_A       ) if idx not in irrelevant_indices]
 __a							=	[coord for idx, coord in enumerate(_A       ) if idx not in irrelevant_indices]
 __a							=	[coord for idx, coord in enumerate(_A       ) if idx not in irrelevant_indices]
 __a							=	[coord for idx, coord in enumerate(_A       ) if idx not in irrelevant_indices]
 __a							=	[coord for idx, coord in enumerate(_A       ) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 __a							=	[]
 for x, y, w, h in zip(_A				,					_A				,					_A				,					_A       ):
  __a							=	[x, y, x + w, y + h]
  actual_boxes.append(_A       )

 # finally, normalize the bounding boxes
 __a							=	[]
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(_A				,					_A				,					_A       )       )

 assert len(_A       ) == len(_A       ), "Not as many words as there are bounding boxes"

 return words, normalized_boxes






class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BILINEAR  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   float = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]] = None  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = ""  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 2_24, "width": 2_24}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_value
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __a							=	image_std if image_std is not None else IMAGENET_STANDARD_STD
   __a							=	apply_ocr
   __a							=	ocr_lang
   __a							=	tesseract_config

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BILINEAR  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Dict  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   float = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]] = None  ,				__SCREAMING_SNAKE_CASE			:   Union[float, Iterable[float]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE							)
   __a							=	resample if resample is not None else self.resample
   __a							=	do_rescale if do_rescale is not None else self.do_rescale
   __a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
   __a							=	do_normalize if do_normalize is not None else self.do_normalize
   __a							=	image_mean if image_mean is not None else self.image_mean
   __a							=	image_std if image_std is not None else self.image_std
   __a							=	apply_ocr if apply_ocr is not None else self.apply_ocr
   __a							=	ocr_lang if ocr_lang is not None else self.ocr_lang
   __a							=	tesseract_config if tesseract_config is not None else self.tesseract_config

   __a							=	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_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("If do_normalize is True, image_mean and image_std must be specified."							)

   # All transformations expect numpy arrays.
   __a							=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

   # Tesseract OCR to get words + normalized bounding boxes
   if apply_ocr:
    requires_backends(self  ,				"pytesseract"							)
    __a							=	[]
    __a							=	[]
    for image in images:
     __a							,  __a							=	apply_tesseract(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     words_batch.append(__SCREAMING_SNAKE_CASE							)
     boxes_batch.append(__SCREAMING_SNAKE_CASE							)

   if do_resize:
    __a							=	[self.resize(image=__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(image=__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(image=__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   if apply_ocr:
    __a							=	words_batch
    __a							=	boxes_batch
   return data



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	original_name.split("."       )[0]
 __a							=	key.split("."       )
 __a							=	int(key_list[key_list.index(_A       ) - 2]       )
 __a							=	int(key_list[key_list.index(_A       ) - 1]       )
 __a							=	orig_block_num - offset

 __a							=	key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,					f"""block.{new_block_num}.{layer_num}.{new_name}"""       )
 return key



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	OrderedDict()
 __a							,  __a							=	0, 0
 for key, value in state_dict.items():
  if key.startswith("network"       ):
   __a							=	key.replace("network"				,					"poolformer.encoder"       )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith("bias"       ) and "patch_embed" not in key:
    patch_emb_offset += 1
   __a							=	key[: key.find("proj"       )]
   __a							=	key.replace(_A				,					f"""patch_embeddings.{total_embed_found}."""       )
   __a							=	key.replace("proj"				,					"projection"       )
   if key.endswith("bias"       ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   __a							=	"poolformer.encoder." + key
  if "mlp.fc1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc1"				,					"output.conv1"       )
  if "mlp.fc2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"mlp.fc2"				,					"output.conv2"       )
  if "norm1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm1"				,					"before_norm"       )
  if "norm2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"norm2"				,					"after_norm"       )
  if "layer_scale_1" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_1"				,					"layer_scale_1"       )
  if "layer_scale_2" in key:
   __a							=	replace_key_with_offset(_A				,					_A				,					"layer_scale_2"				,					"layer_scale_2"       )
  if "head" in key:
   __a							=	key.replace("head"				,					"classifier"       )
  __a							=	value
 return new_state_dict



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )

 return image



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	PoolFormerConfig()

 # set attributes based on model_name
 __a							=	"huggingface/label-files"
 __a							=	model_name[-3:]
 __a							=	1000
 __a							=	"imagenet-1k-id2label.json"
 __a							=	(1, 1000)

 # set config attributes
 __a							=	json.load(open(hf_hub_download(_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()}
 if size == "s12":
  __a							=	[2, 2, 6, 2]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s24":
  __a							=	[4, 4, 12, 4]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	0.9
 elif size == "s36":
  __a							=	[6, 6, 18, 6]
  __a							=	[64, 128, 320, 512]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.9
 elif size == "m36":
  __a							=	[6, 6, 18, 6]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 elif size == "m48":
  __a							=	[8, 8, 24, 8]
  __a							=	[96, 192, 384, 768]
  __a							=	4.0
  __a							=	1E-6
  __a							=	0.95
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # load image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )

 # Prepare image
 __a							=	prepare_img()
 __a							=	image_processor(images=_A				,					return_tensors="pt"       ).pixel_values

 logger.info(f"""Converting model {model_name}..."""       )

 # load original state dict
 __a							=	torch.load(_A				,					map_location=torch.device("cpu"       )       )

 # rename keys
 __a							=	rename_keys(_A       )

 # create HuggingFace model and load state dict
 __a							=	PoolFormerForImageClassification(_A       )
 model.load_state_dict(_A       )
 model.eval()

 # Define image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )
 __a							=	image_processor(images=prepare_img()				,					return_tensors="pt"       ).pixel_values

 # forward pass
 __a							=	model(_A       )
 __a							=	outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __a							=	torch.tensor([-0.3045, -0.6758, -0.4869]       )
 elif size == "s24":
  __a							=	torch.tensor([0.4402, -0.1374, -0.8045]       )
 elif size == "s36":
  __a							=	torch.tensor([-0.6080, -0.5133, -0.5898]       )
 elif size == "m36":
  __a							=	torch.tensor([0.3952, 0.2263, -1.2668]       )
 elif size == "m48":
  __a							=	torch.tensor([0.1167, -0.0656, -0.3423]       )
 else:
  raise ValueError(f"""Size {size} not supported"""       )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]				,					_A				,					atol=1E-2       )

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
 Path(_A       ).mkdir(exist_ok=_A       )
 model.save_pretrained(_A       )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
 image_processor.save_pretrained(_A       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""poolformer_s12""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


