


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
A	:		str    =       {
    '/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 			UpperCamelCase (     __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		list(s_dict.keys()					)
		for key in keys:
				lowercase__					      =		R""".*/layers_(\d+)"""
				lowercase__					      =		key
				if re.match(__magic_name__   ,   __magic_name__					):
						lowercase__					      =		re.sub(R"""layers_(\d+)"""   ,   R"""block/\1/layer"""   ,   __magic_name__					)

				lowercase__					      =		R"""(encoder|decoder)\/"""

				if re.match(__magic_name__   ,   __magic_name__					):
						lowercase__					      =		re.match(__magic_name__   ,   __magic_name__					).groups()
						if groups[0] == "encoder":
								lowercase__					      =		re.sub(R"""/mlp/"""   ,   R"""/1/mlp/"""   ,   __magic_name__					)
								lowercase__					      =		re.sub(R"""/pre_mlp_layer_norm/"""   ,   R"""/1/layer_norm/"""   ,   __magic_name__					)

						elif groups[0] == "decoder":
								lowercase__					      =		re.sub(R"""/mlp/"""   ,   R"""/2/mlp/"""   ,   __magic_name__					)
								lowercase__					      =		re.sub(R"""/pre_mlp_layer_norm/"""   ,   R"""/2/layer_norm/"""   ,   __magic_name__					)

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

				print(f'''{key} -> {new_key}'''					)
				lowercase__					      =		s_dict.pop(__magic_name__					)

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

						s_dict.pop(__magic_name__					)

		return s_dict


A	:		Tuple    =       {
    '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 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[int]					)       ->  Union[str, Any]:
		"""simple docstring"""




		import regex as re

		with open(__magic_name__   ,   """r"""					) as f:
				lowercase__					      =		f.read()

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

		lowercase__					      =		re.findall(R"""(.*activations) = \(\'(.*)\',\)"""   ,   __magic_name__					)[0]
		lowercase__					      =		str(activation[1]					)

		lowercase__					      =		num_experts
		lowercase__					      =		SwitchTransformersConfig(**__magic_name__					)
		return config






def 			UpperCamelCase (     __magic_name__					:							Tuple   ,   __magic_name__					:							str   ,   __magic_name__					:							Union[str, Any]=None   ,   __magic_name__					:							Tuple="./"   ,   __magic_name__					:							Tuple=8					)       ->  Optional[Any]:
		"""simple docstring"""




		print(f'''Loading flax weights from : {flax_checkpoint_path}'''					)
		lowercase__					      =		checkpoints.load_tax_checkpoint(__magic_name__					)

		if gin_file is not None:
				lowercase__					      =		convert_gin_to_config(__magic_name__   ,   __magic_name__					)
		else:
				lowercase__					      =		SwitchTransformersConfig.from_pretrained(__magic_name__					)

		lowercase__					      =		SwitchTransformersForConditionalGeneration(__magic_name__					)

		lowercase__					      =		flax_params["""target"""]
		lowercase__					      =		flatten_dict(__magic_name__   ,   sep="""/"""					)
		lowercase__					      =		rename_keys(__magic_name__					)
		lowercase__					      =		unflatten_dict(__magic_name__   ,   sep="""/"""					)

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

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


if __name__ == "__main__":
	A	:		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')
	A	:		Optional[Any]    =       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,
	)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

