

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Dict )   ->       Optional[int]:
				_UpperCAmelCase       :		List[Any] 		=    old_name

				if "patch_embed" in old_name:
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    old_name.split("." )

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

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

												_UpperCAmelCase       :		int 		=    "last_stage." + trimmed_name

				elif "network" in old_name and re.search(R".\d."  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    old_name.replace("network"  ,      "intermediate_stages" )

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

				return new_name

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Optional[Any] )   ->       List[str]:
				for key in checkpoint.copy().keys():
								_UpperCAmelCase       :		Optional[int] 		=    checkpoint.pop(lowerCAmelCase )
								_UpperCAmelCase       :		List[str] 		=    val

				return checkpoint

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Optional[int] 		=    "http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase       :		int 		=    Image.open(requests.get(lowerCAmelCase  ,      stream=lowerCAmelCase ).raw )

				return image

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Union[str, Any] )   ->       List[str]:
				_UpperCAmelCase       :		Tuple 		=    torch.load(lowerCAmelCase  ,      map_location="cpu" )["model"]
				_UpperCAmelCase       :		str 		=    EfficientFormerConfig.from_json_file(lowerCAmelCase )
				_UpperCAmelCase       :		Union[str, Any] 		=    EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )

				_UpperCAmelCase       :		str 		=    config.depths[-1] - config.num_metaad_blocks + 1
				_UpperCAmelCase       :		Tuple 		=    convert_torch_checkpoint(lowerCAmelCase  ,      lowerCAmelCase )

				model.load_state_dict(lowerCAmelCase )
				model.eval()

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

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

				# original processing pipeline
				_UpperCAmelCase       :		Optional[int] 		=    Compose(
				    [
				        Resize(lowerCAmelCase  ,      interpolation=pillow_resamplings["bicubic"] ),
				        CenterCrop(lowerCAmelCase ),
				        ToTensor(),
				        Normalize(lowerCAmelCase  ,      lowerCAmelCase ),
				    ] )
				_UpperCAmelCase       :		List[Any] 		=    image_transforms(lowerCAmelCase ).unsqueeze(0 )

				assert torch.allclose(lowerCAmelCase  ,      lowerCAmelCase )

				_UpperCAmelCase       :		int 		=    model(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    outputs.logits

				_UpperCAmelCase       :		Optional[int] 		=    (1, 1000)

				if "l1" in model_name:
								_UpperCAmelCase       :		int 		=    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]  ,      lowerCAmelCase  ,      atol=1E-3 )
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								_UpperCAmelCase       :		Tuple 		=    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]  ,      lowerCAmelCase  ,      atol=1E-3 )
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								_UpperCAmelCase       :		str 		=    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(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
				model.save_pretrained(lowerCAmelCase )
				print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
				processor.save_pretrained(lowerCAmelCase )
				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=lowerCAmelCase  ,      )
								processor.push_to_hub(
								    repo_id=F'Bearnardd/{pytorch_dump_path}'  ,      commit_message="Add image processor"  ,      use_temp_dir=lowerCAmelCase  ,      )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      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)

					SCREAMING_SNAKE_CASE_        =      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,
					)





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


