


import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a					  =     logging.get_logger(__name__)

a					  =     {
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

a					  =     {
    'b0': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_2_4,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_4_0,
        'dropout_rate': 0.2,
        'dw_padding': [1_6],
    },
    'b2': {
        'hidden_dim': 1_4_0_8,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_6_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 1_6],
    },
    'b3': {
        'hidden_dim': 1_5_3_6,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_0_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 1_8],
    },
    'b4': {
        'hidden_dim': 1_7_9_2,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_8_0,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_0_4_8,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_5_6,
        'dropout_rate': 0.4,
        'dw_padding': [1_3, 2_7],
    },
    'b6': {
        'hidden_dim': 2_3_0_4,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_2_8,
        'dropout_rate': 0.5,
        'dw_padding': [3_1],
    },
    'b7': {
        'hidden_dim': 2_5_6_0,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_0_0,
        'dropout_rate': 0.5,
        'dw_padding': [1_8],
    },
}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			EfficientNetConfig()
					lowercase_  							=			CONFIG_MAP[model_name]["""hidden_dim"""]
					lowercase_  							=			CONFIG_MAP[model_name]["""width_coef"""]
					lowercase_  							=			CONFIG_MAP[model_name]["""depth_coef"""]
					lowercase_  							=			CONFIG_MAP[model_name]["""image_size"""]
					lowercase_  							=			CONFIG_MAP[model_name]["""dropout_rate"""]
					lowercase_  							=			CONFIG_MAP[model_name]["""dw_padding"""]

					lowercase_  							=			"""huggingface/label-files"""
					lowercase_  							=			"""imagenet-1k-id2label.json"""
					lowercase_  							=			1_0_0_0
					lowercase_  							=			json.load(open(hf_hub_download(UpperCAmelCase__						,							UpperCAmelCase__						,							repo_type="""dataset"""  )						,							"""r"""  )  )
					lowercase_  							=			{int(UpperCAmelCase__  ): v for k, v in idalabel.items()}

					lowercase_  							=			idalabel
					lowercase_  							=			{v: k for k, v in idalabel.items()}
					return config


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					lowercase_  							=			Image.open(requests.get(UpperCAmelCase__						,							stream=UpperCAmelCase__  ).raw  )
					return im


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			CONFIG_MAP[model_name]["""image_size"""]
					lowercase_  							=			EfficientNetImageProcessor(
					    size={"""height""": size, """width""": size}						,							image_mean=[0.485, 0.456, 0.406]						,							image_std=[0.47853944, 0.4732864, 0.47434163]						,							do_center_crop=UpperCAmelCase__						,							)
					return preprocessor


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			[v.split("""_"""  )[0].split("""block"""  )[1] for v in original_param_names if v.startswith("""block"""  )]
					lowercase_  							=			sorted(set(UpperCAmelCase__  )  )
					lowercase_  							=			len(UpperCAmelCase__  )
					lowercase_  							=			{b: str(UpperCAmelCase__  ) for b, i in zip(UpperCAmelCase__						,							range(UpperCAmelCase__  )  )}

					lowercase_  							=			[]
					rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")  )
					rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")  )
					rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")  )
					rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")  )
					rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")  )

					for b in block_names:
										lowercase_  							=			block_name_mapping[b]
										rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')  )
										rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')  )
										rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')  )
										rename_keys.append(
										    (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')  )
										rename_keys.append(
										    (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')  )
										rename_keys.append(
										    (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')  )
										rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')  )
										rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')  )
										rename_keys.append(
										    (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')  )
										rename_keys.append(
										    (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')  )

										rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')  )
										rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')  )
										rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')  )
										rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')  )
										rename_keys.append(
										    (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')  )
										rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')  )
										rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')  )
										rename_keys.append(
										    (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')  )
										rename_keys.append(
										    (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')  )

					rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""")  )
					rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""")  )
					rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""")  )
					rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""")  )
					rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""")  )

					lowercase_  							=			{}
					for item in rename_keys:
										if item[0] in original_param_names:
															lowercase_  							=			"""efficientnet.""" + item[1]

					lowercase_  							=			"""classifier.weight"""
					lowercase_  							=			"""classifier.bias"""
					return key_mapping


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										lowercase_  							=			key_mapping[key]
										if "_conv" in key and "kernel" in key:
															lowercase_  							=			torch.from_numpy(UpperCAmelCase__  ).permute(3						,							2						,							0						,							1  )
										elif "depthwise_kernel" in key:
															lowercase_  							=			torch.from_numpy(UpperCAmelCase__  ).permute(2						,							3						,							0						,							1  )
										elif "kernel" in key:
															lowercase_  							=			torch.from_numpy(np.transpose(UpperCAmelCase__  )  )
										else:
															lowercase_  							=			torch.from_numpy(UpperCAmelCase__  )

										# Replace HF parameters with original TF model parameters
										assert hf_params[hf_key].shape == new_hf_value.shape
										hf_params[hf_key].copy_(UpperCAmelCase__  )


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			model_classes[model_name](
					    include_top=UpperCAmelCase__						,							weights="""imagenet"""						,							input_tensor=UpperCAmelCase__						,							input_shape=UpperCAmelCase__						,							pooling=UpperCAmelCase__						,							classes=1_0_0_0						,							classifier_activation="""softmax"""						,							)

					lowercase_  							=			original_model.trainable_variables
					lowercase_  							=			original_model.non_trainable_variables
					lowercase_  							=			{param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										lowercase_  							=			param.numpy()
					lowercase_  							=			list(tf_params.keys()  )

					# Load HuggingFace model
					lowercase_  							=			get_efficientnet_config(UpperCAmelCase__  )
					lowercase_  							=			EfficientNetForImageClassification(UpperCAmelCase__  ).eval()
					lowercase_  							=			hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("""Converting parameters..."""  )
					lowercase_  							=			rename_keys(UpperCAmelCase__  )
					replace_params(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )

					# Initialize preprocessor and preprocess input image
					lowercase_  							=			convert_image_processor(UpperCAmelCase__  )
					lowercase_  							=			preprocessor(images=prepare_img()						,							return_tensors="""pt"""  )

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										lowercase_  							=			hf_model(**UpperCAmelCase__  )
					lowercase_  							=			outputs.logits.detach().numpy()

					# Original model inference
					lowercase_  							=			False
					lowercase_  							=			CONFIG_MAP[model_name]["""image_size"""]
					lowercase_  							=			prepare_img().resize((image_size, image_size)						,							resample=PIL.Image.NEAREST  )
					lowercase_  							=			image.img_to_array(UpperCAmelCase__  )
					lowercase_  							=			np.expand_dims(UpperCAmelCase__						,							axis=0  )
					lowercase_  							=			original_model.predict(UpperCAmelCase__  )

					# Check whether original and HF model outputs match  -> np.allclose
					assert np.allclose(UpperCAmelCase__						,							UpperCAmelCase__						,							atol=1e-3  ), "The predicted logits are not the same."
					print("""Model outputs match!"""  )

					if save_model:
										# Create folder to save model
										if not os.path.isdir(UpperCAmelCase__  ):
															os.mkdir(UpperCAmelCase__  )
										# Save converted model and image processor
										hf_model.save_pretrained(UpperCAmelCase__  )
										preprocessor.save_pretrained(UpperCAmelCase__  )

					if push_to_hub:
										# Push model and image processor to hub
										print(F'''Pushing converted {model_name} to the hub...'''  )
										lowercase_  							=			F'''efficientnet-{model_name}'''
										preprocessor.push_to_hub(UpperCAmelCase__  )
										hf_model.push_to_hub(UpperCAmelCase__  )


if __name__ == "__main__":
							a					  =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='b0',
							    type=str,
							    help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='hf_model',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--save_model', action='store_true', help='Save model to local')
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

							a					  =     parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


