

"""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
__UpperCamelCase  :      Optional[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   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Any  				=	list(s_dict.keys() )
	for key in keys:
		lowerCAmelCase__	:						List[str]  				=	r'''.*/layers_(\d+)'''
		lowerCAmelCase__	:						Union[str, Any]  				=	key
		if re.match(lowercase__						,     lowercase__ ):
			lowerCAmelCase__	:						Union[str, Any]  				=	re.sub(r'''layers_(\d+)'''						,     r'''block/\1/layer'''						,     lowercase__ )

		lowerCAmelCase__	:						str  				=	r'''(encoder|decoder)\/'''

		if re.match(lowercase__						,     lowercase__ ):
			lowerCAmelCase__	:						List[str]  				=	re.match(lowercase__						,     lowercase__ ).groups()
			if groups[0] == "encoder":
				lowerCAmelCase__	:						Dict  				=	re.sub(r'''/mlp/'''						,     r'''/1/mlp/'''						,     lowercase__ )
				lowerCAmelCase__	:						List[str]  				=	re.sub(r'''/pre_mlp_layer_norm/'''						,     r'''/1/layer_norm/'''						,     lowercase__ )

			elif groups[0] == "decoder":
				lowerCAmelCase__	:						Union[str, Any]  				=	re.sub(r'''/mlp/'''						,     r'''/2/mlp/'''						,     lowercase__ )
				lowerCAmelCase__	:						Optional[Any]  				=	re.sub(r'''/pre_mlp_layer_norm/'''						,     r'''/2/layer_norm/'''						,     lowercase__ )

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				lowerCAmelCase__	:						Union[str, Any]  				=	new_key.replace(lowercase__						,     lowercase__ )

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

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

			s_dict.pop(lowercase__ )

	return s_dict


__UpperCamelCase  :      Optional[Any]							      =		{
    '''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   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	import regex as re

	with open(lowercase__						,     '''r''' ) as f:
		lowerCAmelCase__	:						str  				=	f.read()

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

	lowerCAmelCase__	:						List[Any]  				=	re.findall(r'''(.*activations) = \(\'(.*)\',\)'''						,     lowercase__ )[0]
	lowerCAmelCase__	:						Any  				=	str(activation[1] )

	lowerCAmelCase__	:						Dict  				=	num_experts
	lowerCAmelCase__	:						Optional[Any]  				=	SwitchTransformersConfig(**lowercase__ )
	return config

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_=None						,     A_="./"						,     A_=8 ):
	print(f'Loading flax weights from : {flax_checkpoint_path}' )
	lowerCAmelCase__	:						Tuple  				=	checkpoints.load_tax_checkpoint(lowercase__ )

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

	lowerCAmelCase__	:						List[Any]  				=	SwitchTransformersForConditionalGeneration(lowercase__ )

	lowerCAmelCase__	:						List[Any]  				=	flax_params['''target''']
	lowerCAmelCase__	:						List[str]  				=	flatten_dict(lowercase__						,     sep='''/''' )
	lowerCAmelCase__	:						Dict  				=	rename_keys(lowercase__ )
	lowerCAmelCase__	:						Any  				=	unflatten_dict(lowercase__						,     sep='''/''' )

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

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


if __name__ == "__main__":
							__UpperCamelCase  :      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''')
							__UpperCamelCase  :      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 os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(lowercase__   , lowercase__	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)
				_graph_replace_input_with(node_proto.attribute[1].g   , lowercase__   , lowercase__	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				__lowercase=			inits[i].name
				__lowercase=			inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

		__lowercase=			set()
		__lowercase=			{}
		__lowercase=			[]

		__lowercase=			0

		for i in range(len(lowercase__	)	):
				if i in dup_set:
						continue

				for j in range(i + 1   , len(lowercase__	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]   , inits[j]	):
								dup_set.add(lowercase__	)
								dup_set.add(lowercase__	)

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

								__lowercase=			inits[i].name
								__lowercase=			inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[name_j]
								ind_to_replace.append((j, i)	)

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






