




from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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			    =logging.get_logger(__name__)

def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							return [
							    int(1_0_0_0 * (box[0] / width)  ),
							    int(1_0_0_0 * (box[1] / height)  ),
							    int(1_0_0_0 * (box[2] / width)  ),
							    int(1_0_0_0 * (box[3] / height)  ),
							]

def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase = None  ):
							SCREAMING_SNAKE_CASE_ 						=     tesseract_config if tesseract_config is not None else ''''''

							# apply OCR
							SCREAMING_SNAKE_CASE_ 						=     to_pil_image(_lowerCAmelCase  )
							SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     pil_image.size
							SCREAMING_SNAKE_CASE_ 						=     pytesseract.image_to_data(_lowerCAmelCase	,     lang=_lowerCAmelCase	,     output_type='''dict'''	,     config=_lowerCAmelCase  )
							SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

							return words, normalized_boxes






class 			__magic_name__    (	__lowerCamelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				List[str]						   =       ['pixel_values']


				def __init__(				self: int		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: Dict[str, int] = None		,       _lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR		,       _lowerCamelCase: bool = True		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[str] = ""		,       **_lowerCamelCase: str		,       ):
											super().__init__(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_ 						=     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_ 						=     do_resize
											SCREAMING_SNAKE_CASE_ 						=     size
											SCREAMING_SNAKE_CASE_ 						=     resample
											SCREAMING_SNAKE_CASE_ 						=     apply_ocr
											SCREAMING_SNAKE_CASE_ 						=     ocr_lang
											SCREAMING_SNAKE_CASE_ 						=     tesseract_config


				def      _A				(				self: Tuple		,       _lowerCamelCase: np.ndarray		,       _lowerCamelCase: Dict[str, int]		,       _lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR		,       _lowerCamelCase: Optional[Union[str, ChannelDimension]] = None		,       **_lowerCamelCase: Optional[Any]		,       ):
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)
											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()}"		)
											SCREAMING_SNAKE_CASE_ 						=     (size['''height'''], size['''width'''])
											return resize(_lowerCamelCase		,       size=_lowerCamelCase		,       resample=_lowerCamelCase		,       data_format=_lowerCamelCase		,       **_lowerCamelCase		)





				def      _A				(				self: Union[str, Any]		,       _lowerCamelCase: ImageInput		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: Dict[str, int] = None		,       _lowerCamelCase: PILImageResampling = None		,       _lowerCamelCase: bool = None		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[str] = None		,       _lowerCamelCase: Optional[Union[str, TensorType]] = None		,       _lowerCamelCase: ChannelDimension = ChannelDimension.FIRST		,       **_lowerCamelCase: Dict		,       ):
											SCREAMING_SNAKE_CASE_ 						=     do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_ 						=     size if size is not None else self.size
											SCREAMING_SNAKE_CASE_ 						=     get_size_dict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_ 						=     resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_ 						=     apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE_ 						=     ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE_ 						=     tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE_ 						=     make_list_of_images(_lowerCamelCase		)

											if not valid_images(_lowerCamelCase		):
																		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.'''		)

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

											if apply_ocr:
																		requires_backends(self		,       '''pytesseract'''		)
																		SCREAMING_SNAKE_CASE_ 						=     []
																		SCREAMING_SNAKE_CASE_ 						=     []
																		for image in images:
																									SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_ 						=     apply_tesseract(_lowerCamelCase		,       _lowerCamelCase		,       _lowerCamelCase		)
																									words_batch.append(_lowerCamelCase		)
																									boxes_batch.append(_lowerCamelCase		)

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

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											SCREAMING_SNAKE_CASE_ 						=     [flip_channel_order(_lowerCamelCase		) for image in images]
											SCREAMING_SNAKE_CASE_ 						=     [to_channel_dimension_format(_lowerCamelCase		,       _lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_ 						=     BatchFeature(data={'''pixel_values''': images}		,       tensor_type=_lowerCamelCase		)

											if apply_ocr:
																		SCREAMING_SNAKE_CASE_ 						=     words_batch
																		SCREAMING_SNAKE_CASE_ 						=     boxes_batch
											return data




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def    a      ( a		,						a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= old_name

			if "patch_embed" in old_name:
						SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= old_name.split('''.'''	)

						if layer == "0":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''0'''		,						'''convolution1'''	)
						elif layer == "1":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''1'''		,						'''batchnorm_before'''	)
						elif layer == "3":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''3'''		,						'''convolution2'''	)
						else:
									SCREAMING_SNAKE_CASE     						= old_name.replace('''4'''		,						'''batchnorm_after'''	)

			if "network" in old_name and re.search(r'''\d\.\d'''		,						a	):
						SCREAMING_SNAKE_CASE     						= r'''\b\d{2}\b'''
						if bool(re.search(a		,						a	)	):
									SCREAMING_SNAKE_CASE     						= re.search(r'''\d\.\d\d.'''		,						a	).group()
						else:
									SCREAMING_SNAKE_CASE     						= re.search(r'''\d\.\d.'''		,						a	).group()
						if int(match[0]	) < 6:
									SCREAMING_SNAKE_CASE     						= old_name.replace(a		,						''''''	)
									SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]	)
									SCREAMING_SNAKE_CASE     						= '''intermediate_stages.''' + trimmed_name
						else:
									SCREAMING_SNAKE_CASE     						= old_name.replace(a		,						''''''	)
									if int(match[2]	) < num_meta4D_last_stage:
												SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						'''meta4D_layers.blocks.''' + match[2]	)
									else:
												SCREAMING_SNAKE_CASE     						= str(int(match[2]	) - num_meta4D_last_stage	)
												SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						'''meta3D_layers.blocks.''' + layer_index	)
												if "norm1" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''norm1'''		,						'''layernorm1'''	)
												elif "norm2" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''norm2'''		,						'''layernorm2'''	)
												elif "fc1" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''fc1'''		,						'''linear_in'''	)
												elif "fc2" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''fc2'''		,						'''linear_out'''	)

									SCREAMING_SNAKE_CASE     						= '''last_stage.''' + trimmed_name

			elif "network" in old_name and re.search(r'''.\d.'''		,						a	):
						SCREAMING_SNAKE_CASE     						= old_name.replace('''network'''		,						'''intermediate_stages'''	)

			if "fc" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''fc'''		,						'''convolution'''	)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm1'''		,						'''batchnorm_before'''	)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm2'''		,						'''batchnorm_after'''	)
			if "proj" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''proj'''		,						'''projection'''	)
			if "dist_head" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''dist_head'''		,						'''distillation_classifier'''	)
			elif "head" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''head'''		,						'''classifier'''	)
			elif "patch_embed" in new_name:
						SCREAMING_SNAKE_CASE     						= '''efficientformer.''' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm'''		,						'''layernorm'''	)
						SCREAMING_SNAKE_CASE     						= '''efficientformer.''' + new_name
			else:
						SCREAMING_SNAKE_CASE     						= '''efficientformer.encoder.''' + new_name

			return new_name


def    a      ( a		,						a	)     ->Any:




			'''simple docstring'''


			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE     						= checkpoint.pop(a	)
						SCREAMING_SNAKE_CASE     						= val

			return checkpoint


def    a      ( )     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			SCREAMING_SNAKE_CASE     						= Image.open(requests.get(a		,						stream=a	).raw	)

			return image


def    a      ( a		,						a		,						a		,						a	)     ->Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= torch.load(a		,						map_location='''cpu'''	)['''model''']
			SCREAMING_SNAKE_CASE     						= EfficientFormerConfig.from_json_file(a	)
			SCREAMING_SNAKE_CASE     						= EfficientFormerForImageClassificationWithTeacher(a	)
			SCREAMING_SNAKE_CASE     						= '''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

			SCREAMING_SNAKE_CASE     						= config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE     						= convert_torch_checkpoint(a		,						a	)

			model.load_state_dict(a	)
			model.eval()

			SCREAMING_SNAKE_CASE     						= {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			# prepare image
			SCREAMING_SNAKE_CASE     						= prepare_img()
			SCREAMING_SNAKE_CASE     						= 256
			SCREAMING_SNAKE_CASE     						= 224
			SCREAMING_SNAKE_CASE     						= EfficientFormerImageProcessor(
			    size={'''shortest_edge''': image_size}		,						crop_size={'''height''': crop_size, '''width''': crop_size}		,						resample=pillow_resamplings['''bicubic''']		,						)
			SCREAMING_SNAKE_CASE     						= processor(images=a		,						return_tensors='''pt'''	).pixel_values

			# original processing pipeline
			SCREAMING_SNAKE_CASE     						= Compose(
			    [
			        Resize(a		,						interpolation=pillow_resamplings['''bicubic''']	),
			        CenterCrop(a	),
			        ToTensor(),
			        Normalize(a		,						a	),
			    ]	)
			SCREAMING_SNAKE_CASE     						= image_transforms(a	).unsqueeze(0	)

			assert torch.allclose(a		,						a	)

			SCREAMING_SNAKE_CASE     						= model(a	)
			SCREAMING_SNAKE_CASE     						= outputs.logits

			SCREAMING_SNAKE_CASE     						= (1, 1000)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]	)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""	)

			# Save Checkpoints
			Path(a	).mkdir(exist_ok=a	)
			model.save_pretrained(a	)
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
			processor.save_pretrained(a	)
			print(F"""Processor successfuly saved at {pytorch_dump_path}"""	)

			if push_to_hub:
						print('''Pushing model to the hub...'''	)

						model.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""		,						commit_message='''Add model'''		,						use_temp_dir=a		,						)
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""		,						commit_message='''Add image processor'''		,						use_temp_dir=a		,						)


if __name__ == "__main__":
							__lowerCAmelCase					   =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to EfficientFormer pytorch checkpoint.',
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for EfficientFormer model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)

							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)
							parser.set_defaults(push_to_hub=True)

							__lowerCAmelCase					   =       parser.parse_args()
							convert_efficientformer_checkpoint(
							    checkpoint_path=args.pytorch_model_path,
							    efficientformer_config_file=args.config_file,
							    pytorch_dump_path=args.pytorch_dump_path,
							    push_to_hub=args.push_to_hub,
							)