

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,
			)






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Optional[int]      )     ->	Dict:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

					_a      :       str   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]      )
					_a      :       Tuple   =						k_tmp.reshape(k_tmp.shape[0]						,k_tmp.shape[1] * k_tmp.shape[2]      )
					_a      :       Any   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]      )
					_a      :       Dict   =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,o_tmp.shape[2]      )
					_a      :       Union[str, Any]   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]      )
					_a      :       Any   =						q_tmp.reshape(q_tmp.shape[0]						,q_tmp.shape[1] * q_tmp.shape[2]      )
					_a      :       Tuple   =						np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]      )
					_a      :       int   =						v_tmp.reshape(v_tmp.shape[0]						,v_tmp.shape[1] * v_tmp.shape[2]      )
					return k, o, q, v



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Union[str, Any]						,__a :     List[Any]						,__a :     Any=False      )     ->	Any:

					"""simple docstring"""

					if split_mlp_wi:
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_a      :       List[str]   =						(wi_a, wi_a)
					else:
										_a      :       List[str]   =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.join(__a      ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

					# Encoder.
					for i in range(__a      ):
										# Block i, layer 0 (Self Attention).
										_a      :       Optional[Any]   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_attention_layer_norm'''      )
										_a ,   _a ,   _a ,   _a      :       List[str]   =						tax_attention_lookup(__a						,__a						,'''encoder'''						,'''attention'''      )
										_a      :       List[str]   =						layer_norm
										_a      :       Optional[Any]   =						k.T
										_a      :       str   =						o.T
										_a      :       List[Any]   =						q.T
										_a      :       Tuple   =						v.T

										# Block i, layer 1 (MLP).
										_a      :       str   =						tax_layer_norm_lookup(__a						,__a						,'''encoder'''						,'''pre_mlp_layer_norm'''      )
										_a ,   _a      :       Any   =						tax_mlp_lookup(__a						,__a						,'''encoder'''						,__a      )
										_a      :       str   =						layer_norm
										if split_mlp_wi:
															_a      :       List[Any]   =						wi[0].T
															_a      :       Any   =						wi[1].T
										else:
															_a      :       Any   =						wi.T
										_a      :       Optional[Any]   =						wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															_a      :       Dict   =						tax_relpos_bias_lookup(
															    __a						,__a						,'''encoder'''      ).T

					_a      :       List[str]   =						old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

					if not is_encoder_only:
										# Decoder.
										for i in range(__a      ):
															# Block i, layer 0 (Self Attention).
															_a      :       Union[str, Any]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_self_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       Optional[Any]   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''self_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Dict   =						k.T
															_a      :       str   =						o.T
															_a      :       str   =						q.T
															_a      :       List[str]   =						v.T

															# Block i, layer 1 (Cross Attention).
															_a      :       Any   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_cross_attention_layer_norm'''      )
															_a ,   _a ,   _a ,   _a      :       str   =						tax_attention_lookup(__a						,__a						,'''decoder'''						,'''encoder_decoder_attention'''      )
															_a      :       Optional[Any]   =						layer_norm
															_a      :       Optional[int]   =						k.T
															_a      :       Dict   =						o.T
															_a      :       str   =						q.T
															_a      :       int   =						v.T

															# Block i, layer 2 (MLP).
															_a      :       Optional[int]   =						tax_layer_norm_lookup(__a						,__a						,'''decoder'''						,'''pre_mlp_layer_norm'''      )
															_a ,   _a      :       Tuple   =						tax_mlp_lookup(__a						,__a						,'''decoder'''						,__a      )
															_a      :       Optional[Any]   =						layer_norm
															if split_mlp_wi:
																				_a      :       List[str]   =						wi[0].T
																				_a      :       List[Any]   =						wi[1].T
															else:
																				_a      :       Dict   =						wi.T
															_a      :       str   =						wo.T

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       Tuple   =						old['''decoder/decoder_norm/scale''']

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_a      :       Any   =						state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_a      :       Optional[int]   =						state_dict['''shared.weight''']

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print('''Using shared word embeddings as lm_head.'''      )
															_a      :       str   =						state_dict['''shared.weight''']

					return state_dict



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Dict						,__a :     Union[str, Any]						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.from_json_file(__a      )
					print(F"""Building PyTorch model from configuration: {config}"""      )
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(__a						,__a						,__a						,__a						,__a      )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					model.save_pretrained(__a      )

					# Verify that we can load the checkpoint.
					model.from_pretrained(__a      )
					print('''Done'''      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
			# Required parameters
			parser.add_argument(
			    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
			)
			parser.add_argument(
			    '''--scalable_attention''',
			    action='''store_true''',
			    help='''Whether the model uses scaled attention (umt5 model)''',
			    default=False,
			)
			a__												=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)



