
"""simple docstring"""




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__				=					{
    '/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 							_lowerCamelCase			(			__a     ):
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		SCREAMING_SNAKE_CASE_    			=			list(s_dict.keys()     )
		for key in keys:
				SCREAMING_SNAKE_CASE_    			=			r'''.*/layers_(\d+)'''
				SCREAMING_SNAKE_CASE_    			=			key
				if re.match(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			re.sub(r'''layers_(\d+)''',				r'''block/\1/layer''',				__a     )

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

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

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

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

				print(F'{key} -> {new_key}'     )
				SCREAMING_SNAKE_CASE_    			=			s_dict.pop(__a     )

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_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:
				SCREAMING_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:
						SCREAMING_SNAKE_CASE_    			=			s_dict[key].shape[0]
						SCREAMING_SNAKE_CASE_    			=			s_dict[key]
						for idx in range(__a     ):
								SCREAMING_SNAKE_CASE_    			=			expert_weihts[idx]
								print(F'{key} -> {key.replace("expert/",				"nested fstring"     )}'     )

						s_dict.pop(__a     )

		return s_dict


lowerCAmelCase__				=					{
    '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 							_lowerCamelCase			(			__a,				__a     ):
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(__a,				'''r'''     ) as f:
				SCREAMING_SNAKE_CASE_    			=			f.read()

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

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

		SCREAMING_SNAKE_CASE_    			=			num_experts
		SCREAMING_SNAKE_CASE_    			=			SwitchTransformersConfig(**__a     )
		return config



def 							_lowerCamelCase			(			__a,				__a,				__a=None,				__a="./",				__a=8     ):
		# Initialise PyTorch model

		print(F'Loading flax weights from : {flax_checkpoint_path}'     )
		SCREAMING_SNAKE_CASE_    			=			checkpoints.load_tax_checkpoint(__a     )

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_    			=			convert_gin_to_config(__a,				__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			SwitchTransformersConfig.from_pretrained(__a     )

		SCREAMING_SNAKE_CASE_    			=			SwitchTransformersForConditionalGeneration(__a     )

		SCREAMING_SNAKE_CASE_    			=			flax_params['''target''']
		SCREAMING_SNAKE_CASE_    			=			flatten_dict(__a,				sep='''/'''     )
		SCREAMING_SNAKE_CASE_    			=			rename_keys(__a     )
		SCREAMING_SNAKE_CASE_    			=			unflatten_dict(__a,				sep='''/'''     )

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

		print(F'Save PyTorch model to {pytorch_dump_path}'     )
		pt_model.save_pretrained(__a     )


if __name__ == "__main__":
						lowerCAmelCase__				=					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__				=					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,
						)

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



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

		SCREAMING_SNAKE_CASE_    			=			'''std_conv''' if '''bit''' in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		SCREAMING_SNAKE_CASE_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



def 							_lowerCamelCase			(			__a     ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''stem.conv''',				'''bit.embedder.convolution'''     )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks''',				'''layers'''     )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''head.fc''',				'''classifier.1'''     )
		if name.startswith('''norm'''     ):
				SCREAMING_SNAKE_CASE_    			=			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_    			=			'''bit.encoder.''' + name

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

		# create image processor
		SCREAMING_SNAKE_CASE_    			=			create_transform(**resolve_data_config({},				model=__a     )     )
		SCREAMING_SNAKE_CASE_    			=			transform.transforms

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

		SCREAMING_SNAKE_CASE_    			=			BitImageProcessor(
		    do_resize=__a,				size={'''shortest_edge''': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=__a,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},				do_normalize=__a,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

		# verify pixel values
		assert torch.allclose(__a,				__a     )

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

		print('''Logits:''',				logits[0, :3]     )
		print('''Predicted class:''',				model.config.idalabel[logits.argmax(-1     ).item()]     )
		SCREAMING_SNAKE_CASE_    			=			timm_model(__a     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a,				outputs.logits,				atol=1E-3     )
		print('''Looks ok!'''     )

		if pytorch_dump_folder_path is not None:
				Path(__a     ).mkdir(exist_ok=__a     )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
				model.save_pretrained(__a     )
				processor.save_pretrained(__a     )

		if push_to_hub:
				print(F'Pushing model {model_name} and processor to the hub'     )
				model.push_to_hub(F'ybelkada/{model_name}'     )
				processor.push_to_hub(F'ybelkada/{model_name}'     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT timm model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						lowerCAmelCase__				=					parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)