


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__  :List[Any]             =							{
    '''/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__:		Optional[int]	)  ->		Optional[int]:


							'''simple docstring'''





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

														_UpperCAmelCase					=      R'(encoder|decoder)\/'

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

																					elif groups[0] == "decoder":
																												_UpperCAmelCase					=      re.sub(R'/mlp/'      ,  R'/2/mlp/'      ,  a__	)
																												_UpperCAmelCase					=      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:
																												_UpperCAmelCase					=      new_key.replace(a__      ,  a__	)

														print(F'''{key} -> {new_key}'''	)
														_UpperCAmelCase					=      s_dict.pop(a__	)

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

																					s_dict.pop(a__	)

							return s_dict


lowerCAmelCase__  :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 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							import regex as re

							with open(a__      ,  'r'	) as f:
														_UpperCAmelCase					=      f.read()

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

							_UpperCAmelCase					=      re.findall(R'(.*activations) = \(\'(.*)\',\)'      ,  a__	)[0]
							_UpperCAmelCase					=      str(activation[1]	)

							_UpperCAmelCase					=      num_experts
							_UpperCAmelCase					=      SwitchTransformersConfig(**a__	)
							return config

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[str]      ,  a__:		Any=None      ,  a__:		List[Any]="./"      ,  a__:		List[str]=8	)  ->		List[str]:


							'''simple docstring'''





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

							if gin_file is not None:
														_UpperCAmelCase					=      convert_gin_to_config(a__      ,  a__	)
							else:
														_UpperCAmelCase					=      SwitchTransformersConfig.from_pretrained(a__	)

							_UpperCAmelCase					=      SwitchTransformersForConditionalGeneration(a__	)

							_UpperCAmelCase					=      flax_params['target']
							_UpperCAmelCase					=      flatten_dict(a__      ,  sep='/'	)
							_UpperCAmelCase					=      rename_keys(a__	)
							_UpperCAmelCase					=      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__  :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''')
		lowerCAmelCase__  :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,
		)









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							argparse.ArgumentParser(
		    description=(
		        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
		        ''' Distillation'''
		    )
		)
		parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
		parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
		parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
		parser.add_argument('''--vocab_transform''', action='''store_true''')
		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						lowerCAmelCase__  :int             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

		lowerCAmelCase__  :Optional[int]             =							state_dict['''cls.predictions.decoder.weight''']
		lowerCAmelCase__  :List[str]             =							state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[f'''cls.predictions.transform.dense.{w}''']
						lowerCAmelCase__  :List[str]             =							state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

		print(f'''N layers selected for distillation: {std_idx}''')
		print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)
