



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				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Union[str, Any] )  ->Dict:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		old_name

			if "patch_embed" in old_name:
						lowerCamelCase__ :      Tuple								=		old_name.split(""".""" )

						if layer == "0":
									lowerCamelCase__ :      str								=		old_name.replace("""0"""      ,			"""convolution1""" )
						elif layer == "1":
									lowerCamelCase__ :      List[str]								=		old_name.replace("""1"""      ,			"""batchnorm_before""" )
						elif layer == "3":
									lowerCamelCase__ :      int								=		old_name.replace("""3"""      ,			"""convolution2""" )
						else:
									lowerCamelCase__ :      Optional[Any]								=		old_name.replace("""4"""      ,			"""batchnorm_after""" )

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

									lowerCamelCase__ :      str								=		"""last_stage.""" + trimmed_name

			elif "network" in old_name and re.search(r""".\d."""      ,			UpperCamelCase ):
						lowerCamelCase__ :      Union[str, Any]								=		old_name.replace("""network"""      ,			"""intermediate_stages""" )

			if "fc" in new_name:
						lowerCamelCase__ :      int								=		new_name.replace("""fc"""      ,			"""convolution""" )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						lowerCamelCase__ :      str								=		new_name.replace("""norm1"""      ,			"""batchnorm_before""" )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						lowerCamelCase__ :      Union[str, Any]								=		new_name.replace("""norm2"""      ,			"""batchnorm_after""" )
			if "proj" in new_name:
						lowerCamelCase__ :      List[str]								=		new_name.replace("""proj"""      ,			"""projection""" )
			if "dist_head" in new_name:
						lowerCamelCase__ :      Union[str, Any]								=		new_name.replace("""dist_head"""      ,			"""distillation_classifier""" )
			elif "head" in new_name:
						lowerCamelCase__ :      str								=		new_name.replace("""head"""      ,			"""classifier""" )
			elif "patch_embed" in new_name:
						lowerCamelCase__ :      Any								=		"""efficientformer.""" + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						lowerCamelCase__ :      Tuple								=		new_name.replace("""norm"""      ,			"""layernorm""" )
						lowerCamelCase__ :      Tuple								=		"""efficientformer.""" + new_name
			else:
						lowerCamelCase__ :      int								=		"""efficientformer.encoder.""" + new_name

			return new_name

def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->Any:



			'''simple docstring'''







			for key in checkpoint.copy().keys():
						lowerCamelCase__ :      Union[str, Any]								=		checkpoint.pop(UpperCamelCase )
						lowerCamelCase__ :      Union[str, Any]								=		val

			return checkpoint

def      _A				()  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase__ :      Dict								=		Image.open(requests.get(UpperCamelCase      ,			stream=UpperCamelCase ).raw )

			return image

def      _A				(UpperCamelCase		:      Path      ,			UpperCamelCase		:      Path      ,			UpperCamelCase		:      Path      ,			UpperCamelCase		:      bool )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		torch.load(UpperCamelCase      ,			map_location="""cpu""" )["""model"""]
			lowerCamelCase__ :      Optional[Any]								=		EfficientFormerConfig.from_json_file(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		EfficientFormerForImageClassificationWithTeacher(UpperCamelCase )
			lowerCamelCase__ :      int								=		"""_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

			lowerCamelCase__ :      List[str]								=		config.depths[-1] - config.num_metaad_blocks + 1
			lowerCamelCase__ :      Optional[Any]								=		convert_torch_checkpoint(UpperCamelCase      ,			UpperCamelCase )

			model.load_state_dict(UpperCamelCase )
			model.eval()

			lowerCamelCase__ :      str								=		{
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			# prepare image
			lowerCamelCase__ :      Optional[int]								=		prepare_img()
			lowerCamelCase__ :      str								=		256
			lowerCamelCase__ :      Any								=		224
			lowerCamelCase__ :      Tuple								=		EfficientFormerImageProcessor(
			    size={"""shortest_edge""": image_size}      ,			crop_size={"""height""": crop_size, """width""": crop_size}      ,			resample=pillow_resamplings["""bicubic"""]      ,			)
			lowerCamelCase__ :      List[Any]								=		processor(images=UpperCamelCase      ,			return_tensors="""pt""" ).pixel_values

			# original processing pipeline
			lowerCamelCase__ :      Union[str, Any]								=		Compose(
			    [
			        Resize(UpperCamelCase      ,			interpolation=pillow_resamplings["""bicubic"""] ),
			        CenterCrop(UpperCamelCase ),
			        ToTensor(),
			        Normalize(UpperCamelCase      ,			UpperCamelCase ),
			    ] )
			lowerCamelCase__ :      Union[str, Any]								=		image_transforms(UpperCamelCase ).unsqueeze(0 )

			assert torch.allclose(UpperCamelCase      ,			UpperCamelCase )

			lowerCamelCase__ :      int								=		model(UpperCamelCase )
			lowerCamelCase__ :      List[Any]								=		outputs.logits

			lowerCamelCase__ :      str								=		(1, 1000)

			if "l1" in model_name:
						lowerCamelCase__ :      List[str]								=		torch.Tensor(
						    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
						assert torch.allclose(logits[0, :10]      ,			UpperCamelCase      ,			atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						lowerCamelCase__ :      Any								=		torch.Tensor(
						    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
						assert torch.allclose(logits[0, :10]      ,			UpperCamelCase      ,			atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						lowerCamelCase__ :      Optional[Any]								=		torch.Tensor(
						    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
						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(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
			model.save_pretrained(UpperCamelCase )
			print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
			processor.save_pretrained(UpperCamelCase )
			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=UpperCamelCase      ,			)
						processor.push_to_hub(
						    repo_id=f"Bearnardd/{pytorch_dump_path}"      ,			commit_message="""Add image processor"""      ,			use_temp_dir=UpperCamelCase      ,			)


if __name__ == "__main__":
				_lowercase     							=						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)

				_lowercase     							=						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,
				)








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



