



"""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
A_						:    Tuple				=      {
    '/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  __snake_case							(	__A      :						Optional[Any]     )     ->       Union[str, Any]:






				'''simple docstring'''
				# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
				# the original model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     list(s_dict.keys()     )
				for key in keys:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     r'.*/layers_(\d+)'
								SCREAMING_SNAKE_CASE						:							Dict							=     key
								if re.match(__A							,		__A     ):
												SCREAMING_SNAKE_CASE						:							Dict							=     re.sub(r'layers_(\d+)'							,		r'block/\1/layer'							,		__A     )

								SCREAMING_SNAKE_CASE						:							Tuple							=     r'(encoder|decoder)\/'

								if re.match(__A							,		__A     ):
												SCREAMING_SNAKE_CASE						:							str							=     re.match(__A							,		__A     ).groups()
												if groups[0] == "encoder":
																SCREAMING_SNAKE_CASE						:							Optional[Any]							=     re.sub(r'/mlp/'							,		r'/1/mlp/'							,		__A     )
																SCREAMING_SNAKE_CASE						:							Dict							=     re.sub(r'/pre_mlp_layer_norm/'							,		r'/1/layer_norm/'							,		__A     )

												elif groups[0] == "decoder":
																SCREAMING_SNAKE_CASE						:							Tuple							=     re.sub(r'/mlp/'							,		r'/2/mlp/'							,		__A     )
																SCREAMING_SNAKE_CASE						:							int							=     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						:							Optional[int]							=     new_key.replace(__A							,		__A     )

								print(F"""{key} -> {new_key}"""     )
								SCREAMING_SNAKE_CASE						:							List[str]							=     s_dict.pop(__A     )

				if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
								SCREAMING_SNAKE_CASE						:							int							=     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						:							List[str]							=     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						:							Optional[int]							=     s_dict[key].shape[0]
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     s_dict[key]
												for idx in range(__A     ):
																SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     expert_weihts[idx]
																print(F"""{key} -> {key.replace("expert/"							,		"nested fstring"     )}"""     )

												s_dict.pop(__A     )

				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  __snake_case							(	__A      :						Tuple							,		__A      :						Any     )     ->       Tuple:






				'''simple docstring'''
				# Convert a google style config to the hugging face fromat
				import regex as re

				with open(__A							,		'r'     ) as f:
								SCREAMING_SNAKE_CASE						:							Any							=     f.read()

				SCREAMING_SNAKE_CASE						:							str							=     re.findall(r'(.*) = ([0-9.]*)'							,		__A     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     {}
				for param, value in regex_match:
								if param in GIN_TO_CONFIG_MAPPING and value != "":
												SCREAMING_SNAKE_CASE						:							List[Any]							=     float(__A     ) if '.' in value else int(__A     )

				SCREAMING_SNAKE_CASE						:							Dict							=     re.findall(r'(.*activations) = \(\'(.*)\',\)'							,		__A     )[0]
				SCREAMING_SNAKE_CASE						:							str							=     str(activation[1]     )

				SCREAMING_SNAKE_CASE						:							Any							=     num_experts
				SCREAMING_SNAKE_CASE						:							Dict							=     SwitchTransformersConfig(**__A     )
				return config
def  __snake_case							(	__A      :						Optional[int]							,		__A      :						Union[str, Any]							,		__A      :						int=None							,		__A      :						Optional[Any]="./"							,		__A      :						Optional[Any]=8     )     ->       List[str]:






				'''simple docstring'''
				# Initialise PyTorch model

				print(F"""Loading flax weights from : {flax_checkpoint_path}"""     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     checkpoints.load_tax_checkpoint(__A     )

				if gin_file is not None:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     convert_gin_to_config(__A							,		__A     )
				else:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     SwitchTransformersConfig.from_pretrained(__A     )

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     SwitchTransformersForConditionalGeneration(__A     )

				SCREAMING_SNAKE_CASE						:							Dict							=     flax_params['target']
				SCREAMING_SNAKE_CASE						:							str							=     flatten_dict(__A							,		sep='/'     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     rename_keys(__A     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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__":
							A_						:    Union[str, 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_						:    List[str]				=      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 intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[Any]				=      argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A_						:    Tuple				=      parser.parse_args()


A_						:    Optional[int]				=      'cpu'
A_						:    Dict				=      'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A_						:    List[str]				=      'path-to-your-trained-model'
A_						:    Any				=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
							A_						:    Tuple				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A_						:    Tuple				=      pipe.to(device)

# to channels last
A_						:    List[str]				=      pipe.unet.to(memory_format=torch.channels_last)
A_						:    List[str]				=      pipe.vae.to(memory_format=torch.channels_last)
A_						:    Optional[int]				=      pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
							A_						:    Union[str, Any]				=      pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A_						:    List[Any]				=      torch.randn(2, 4, 64, 64)
A_						:    int				=      torch.rand(1) * 999
A_						:    int				=      torch.randn(2, 77, 768)
A_						:    Tuple				=      (sample, timestep, encoder_hidden_status)
try:
							A_						:    Any				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
							A_						:    List[str]				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    str				=      ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    Optional[int]				=      ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
							A_						:    List[str]				=      ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A_						:    Union[str, Any]				=      666
A_						:    Optional[Any]				=      torch.Generator(device).manual_seed(seed)
A_						:    Union[str, Any]				=      {'generator': generator}
if args.steps is not None:
							A_						:    int				=      args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
							A_						:    int				=      pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')





