
import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_lowerCamelCase	:    Dict          =      {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}






def 	a_		(	__lowercase       :     List[str]			)			->  Any:
			_snake_case          =      list(s_dict.keys()			)
			for key in keys:
						_snake_case          =      r'.*/layers_(\d+)'
						_snake_case          =      key
						if re.match(_lowercase	, _lowercase			):
									_snake_case          =      re.sub(r'layers_(\d+)'	, r'block/\1/layer'	, _lowercase			)

						_snake_case          =      r'(encoder|decoder)\/'

						if re.match(_lowercase	, _lowercase			):
									_snake_case          =      re.match(_lowercase	, _lowercase			).groups()
									if groups[0] == "encoder":
												_snake_case          =      re.sub(r'/mlp/'	, r'/1/mlp/'	, _lowercase			)
												_snake_case          =      re.sub(r'/pre_mlp_layer_norm/'	, r'/1/layer_norm/'	, _lowercase			)

									elif groups[0] == "decoder":
												_snake_case          =      re.sub(r'/mlp/'	, r'/2/mlp/'	, _lowercase			)
												_snake_case          =      re.sub(r'/pre_mlp_layer_norm/'	, r'/2/layer_norm/'	, _lowercase			)

        # 2. Convert other classic mappings
						for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
									if old_key in new_key:
												_snake_case          =      new_key.replace(_lowercase	, _lowercase			)

						print(f'''{key} -> {new_key}'''			)
						_snake_case          =      s_dict.pop(_lowercase			)

			if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						_snake_case          =      s_dict[
						    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
						].T
			if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						_snake_case          =      s_dict[
						    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
						].T

			# 3. Take extra care of the EXPERTS layer
			for key in list(s_dict.keys()			):
						if "expert" in key:
									_snake_case          =      s_dict[key].shape[0]
									_snake_case          =      s_dict[key]
									for idx in range(_lowercase			):
												_snake_case          =      expert_weihts[idx]
												print(f'''{key} -> {key.replace('expert/'	, 'nested fstring'			)}'''			)

									s_dict.pop(_lowercase			)

			return s_dict


_lowerCamelCase	:    Optional[int]          =      {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}






def 	a_		(	__lowercase       :     int	, __lowercase       :     str			)			->  str:
			import regex as re

			with open(_lowercase	, 'r'			) as f:
						_snake_case          =      f.read()

			_snake_case          =      re.findall(r'(.*) = ([0-9.]*)'	, _lowercase			)
			_snake_case          =      {}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									_snake_case          =      float(_lowercase			) if '.' in value else int(_lowercase			)

			_snake_case          =      re.findall(r'(.*activations) = \(\'(.*)\',\)'	, _lowercase			)[0]
			_snake_case          =      str(activation[1]			)

			_snake_case          =      num_experts
			_snake_case          =      SwitchTransformersConfig(**_lowercase			)
			return config






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     str	, __lowercase       :     Optional[int]=None	, __lowercase       :     str="./"	, __lowercase       :     Any=8			)			->  List[Any]:
			print(f'''Loading flax weights from : {flax_checkpoint_path}'''			)
			_snake_case          =      checkpoints.load_tax_checkpoint(_lowercase			)

			if gin_file is not None:
						_snake_case          =      convert_gin_to_config(_lowercase	, _lowercase			)
			else:
						_snake_case          =      SwitchTransformersConfig.from_pretrained(_lowercase			)

			_snake_case          =      SwitchTransformersForConditionalGeneration(_lowercase			)

			_snake_case          =      flax_params['target']
			_snake_case          =      flatten_dict(_lowercase	, sep='/'			)
			_snake_case          =      rename_keys(_lowercase			)
			_snake_case          =      unflatten_dict(_lowercase	, sep='/'			)

			# Load the flax params in the PT model
			load_flax_weights_in_pytorch_model(_lowercase	, _lowercase			)

			print(f'''Save PyTorch model to {pytorch_dump_path}'''			)
			pt_model.save_pretrained(_lowercase			)


if __name__ == "__main__":
			_lowerCamelCase	:    List[Any]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--switch_t5x_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
			        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
			    ),
			)
			parser.add_argument(
			    '''--gin_file''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
			)
			parser.add_argument(
			    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
			)
			parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
			_lowerCamelCase	:    int          =      parser.parse_args()
			convert_flax_checkpoint_to_pytorch(
			    args.switch_tax_checkpoint_path,
			    args.config_name,
			    args.gin_file,
			    args.pytorch_dump_folder_path,
			    args.num_experts,
			)

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()
_lowerCamelCase	:    int          =      logging.get_logger(__name__)

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

_lowerCamelCase	:    List[str]          =      {
    '''b0''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1_408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1_536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1_792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2_304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2_560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}






def 	a_		(	__lowercase       :     int			)			->  Optional[int]:
			_snake_case          =      EfficientNetConfig()
			_snake_case          =      CONFIG_MAP[model_name]['hidden_dim']
			_snake_case          =      CONFIG_MAP[model_name]['width_coef']
			_snake_case          =      CONFIG_MAP[model_name]['depth_coef']
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      CONFIG_MAP[model_name]['dropout_rate']
			_snake_case          =      CONFIG_MAP[model_name]['dw_padding']

			_snake_case          =      'huggingface/label-files'
			_snake_case          =      'imagenet-1k-id2label.json'
			_snake_case          =      1_000
			_snake_case          =      json.load(open(hf_hub_download(__lowercase	, __lowercase	, repo_type='dataset'			)	, 'r'			)			)
			_snake_case          =      {int(__lowercase			): v for k, v in idalabel.items()}

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






def 	a_		(	)			->  Any:
			_snake_case          =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case          =      Image.open(requests.get(__lowercase	, stream=__lowercase			).raw			)
			return im






def 	a_		(	__lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      EfficientNetImageProcessor(
			    size={'height': size, 'width': size}	, image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	, image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	, do_center_crop=__lowercase	, )
			return preprocessor






def 	a_		(	__lowercase       :     str			)			->  List[Any]:
			_snake_case          =      [v.split('_'			)[0].split('block'			)[1] for v in original_param_names if v.startswith('block'			)]
			_snake_case          =      sorted(set(__lowercase			)			)
			_snake_case          =      len(__lowercase			)
			_snake_case          =      {b: str(__lowercase			) for b, i in zip(__lowercase	, range(__lowercase			)			)}

			_snake_case          =      []
			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:
						_snake_case          =      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')			)

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

			_snake_case          =      'classifier.weight'
			_snake_case          =      'classifier.bias'
			return key_mapping






def 	a_		(	__lowercase       :     Any	, __lowercase       :     Any	, __lowercase       :     Any			)			->  Optional[Any]:
			for key, value in tf_params.items():
						if "normalization" in key:
									continue

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

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






@torch.no_grad()
def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     int	, __lowercase       :     str			)			->  Dict:
			_snake_case          =      model_classes[model_name](
			    include_top=__lowercase	, weights='imagenet'	, input_tensor=__lowercase	, input_shape=__lowercase	, pooling=__lowercase	, classes=1_000	, classifier_activation='softmax'	, )

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

			# Load HuggingFace model
			_snake_case          =      get_efficientnet_config(__lowercase			)
			_snake_case          =      EfficientNetForImageClassification(__lowercase			).eval()
			_snake_case          =      hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print('Converting parameters...'			)
			_snake_case          =      rename_keys(__lowercase			)
			replace_params(__lowercase	, __lowercase	, __lowercase			)

			# Initialize preprocessor and preprocess input image
			_snake_case          =      convert_image_processor(__lowercase			)
			_snake_case          =      preprocessor(images=prepare_img()	, return_tensors='pt'			)

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

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

			# Check whether original and HF model outputs match  -> np.allclose
			assert np.allclose(__lowercase	, __lowercase	, 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(__lowercase			):
									os.mkdir(__lowercase			)
						# Save converted model and image processor
						hf_model.save_pretrained(__lowercase			)
						preprocessor.save_pretrained(__lowercase			)

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


if __name__ == "__main__":
			_lowerCamelCase	:    int          =      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''')

			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)