

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__												=						{
    '''/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					(      __a :     List[str]      )     ->	str:

					"""simple docstring"""

					_a      :       int   =						list(s_dict.keys()      )
					for key in keys:
										_a      :       Union[str, Any]   =						R'''.*/layers_(\d+)'''
										_a      :       List[str]   =						key
										if re.match(__a						,__a      ):
															_a      :       Tuple   =						re.sub(R'''layers_(\d+)'''						,R'''block/\1/layer'''						,__a      )

										_a      :       Union[str, Any]   =						R'''(encoder|decoder)\/'''

										if re.match(__a						,__a      ):
															_a      :       Tuple   =						re.match(__a						,__a      ).groups()
															if groups[0] == "encoder":
																				_a      :       Union[str, Any]   =						re.sub(R'''/mlp/'''						,R'''/1/mlp/'''						,__a      )
																				_a      :       Tuple   =						re.sub(R'''/pre_mlp_layer_norm/'''						,R'''/1/layer_norm/'''						,__a      )

															elif groups[0] == "decoder":
																				_a      :       Dict   =						re.sub(R'''/mlp/'''						,R'''/2/mlp/'''						,__a      )
																				_a      :       Optional[Any]   =						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:
																				_a      :       Tuple   =						new_key.replace(__a						,__a      )

										print(F"""{key} -> {new_key}"""      )
										_a      :       List[Any]   =						s_dict.pop(__a      )

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

															s_dict.pop(__a      )

					return s_dict


a__												=						{
    '''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					(      __a :     int						,__a :     Any      )     ->	Union[str, Any]:

					"""simple docstring"""

					import regex as re

					with open(__a						,'''r'''      ) as f:
										_a      :       List[Any]   =						f.read()

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

					_a      :       Dict   =						re.findall(R'''(.*activations) = \(\'(.*)\',\)'''						,__a      )[0]
					_a      :       str   =						str(activation[1]      )

					_a      :       Tuple   =						num_experts
					_a      :       Tuple   =						SwitchTransformersConfig(**__a      )
					return config



def   __UpperCAmelCase					(      __a :     Dict						,__a :     Optional[int]						,__a :     Dict=None						,__a :     int="./"						,__a :     Union[str, Any]=8      )     ->	Dict:

					"""simple docstring"""

					print(F"""Loading flax weights from : {flax_checkpoint_path}"""      )
					_a      :       List[Any]   =						checkpoints.load_tax_checkpoint(__a      )

					if gin_file is not None:
										_a      :       Tuple   =						convert_gin_to_config(__a						,__a      )
					else:
										_a      :       Optional[int]   =						SwitchTransformersConfig.from_pretrained(__a      )

					_a      :       int   =						SwitchTransformersForConditionalGeneration(__a      )

					_a      :       Optional[int]   =						flax_params['''target''']
					_a      :       Union[str, Any]   =						flatten_dict(__a						,sep='''/'''      )
					_a      :       Any   =						rename_keys(__a      )
					_a      :       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__												=						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__												=						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 gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''block_out_channels''': (32, 64),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 32,
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return (4, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				return (4, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''sample_size''': 32,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (32, 64),
				    '''attention_head_dim''': 32,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       Any     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model_accelerate.config.in_channels  ,	model_accelerate.config.sample_size  ,	model_accelerate.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       int     			=						noise.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				snake_case			,snake_case  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

				snake_case  :       Any     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Dict     			=						noise.to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa  ,	device=UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''block_out_channels''': [32, 64, 64, 64],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1e-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0      ),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

				assert image is not None, "Make sure output is not None"



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

				snake_case  :       Any     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


