




"""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_        =       {
    '/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     ( __UpperCamelCase	):
			# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
			# the original model
			__lowercase			:  List[Any]								=       list(s_dict.keys()	)
			for key in keys:
						__lowercase			:  Optional[int]								=       R'''.*/layers_(\d+)'''
						__lowercase			:  Tuple								=       key
						if re.match(__UpperCamelCase	,				__UpperCamelCase	):
									__lowercase			:  Optional[Any]								=       re.sub(R'''layers_(\d+)'''	,				R'''block/\1/layer'''	,				__UpperCamelCase	)

						__lowercase			:  str								=       R'''(encoder|decoder)\/'''

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

									elif groups[0] == "decoder":
												__lowercase			:  Optional[Any]								=       re.sub(R'''/mlp/'''	,				R'''/2/mlp/'''	,				__UpperCamelCase	)
												__lowercase			:  Dict								=       re.sub(R'''/pre_mlp_layer_norm/'''	,				R'''/2/layer_norm/'''	,				__UpperCamelCase	)

        # 2. Convert other classic mappings
						for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
									if old_key in new_key:
												__lowercase			:  Optional[Any]								=       new_key.replace(__UpperCamelCase	,				__UpperCamelCase	)

						print(f"""{key} -> {new_key}"""	)
						__lowercase			:  Union[str, Any]								=       s_dict.pop(__UpperCamelCase	)

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

									s_dict.pop(__UpperCamelCase	)

			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     ( __UpperCamelCase	,				__UpperCamelCase	):
			# Convert a google style config to the hugging face fromat
			import regex as re

			with open(__UpperCamelCase	,				'''r'''	) as f:
						__lowercase			:  Dict								=       f.read()

			__lowercase			:  Tuple								=       re.findall(R'''(.*) = ([0-9.]*)'''	,				__UpperCamelCase	)
			__lowercase			:  Union[str, Any]								=       {}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									__lowercase			:  Tuple								=       float(__UpperCamelCase	) if '''.''' in value else int(__UpperCamelCase	)

			__lowercase			:  Any								=       re.findall(R'''(.*activations) = \(\'(.*)\',\)'''	,				__UpperCamelCase	)[0]
			__lowercase			:  Optional[int]								=       str(activation[1]	)

			__lowercase			:  int								=       num_experts
			__lowercase			:  Optional[int]								=       SwitchTransformersConfig(**__UpperCamelCase	)
			return config







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=None	,				__UpperCamelCase="./"	,				__UpperCamelCase=8	):
			# Initialise PyTorch model

			print(f"""Loading flax weights from : {flax_checkpoint_path}"""	)
			__lowercase			:  Optional[int]								=       checkpoints.load_tax_checkpoint(__UpperCamelCase	)

			if gin_file is not None:
						__lowercase			:  Union[str, Any]								=       convert_gin_to_config(__UpperCamelCase	,				__UpperCamelCase	)
			else:
						__lowercase			:  List[Any]								=       SwitchTransformersConfig.from_pretrained(__UpperCamelCase	)

			__lowercase			:  Optional[Any]								=       SwitchTransformersForConditionalGeneration(__UpperCamelCase	)

			__lowercase			:  int								=       flax_params['''target''']
			__lowercase			:  List[Any]								=       flatten_dict(__UpperCamelCase	,				sep='''/'''	)
			__lowercase			:  int								=       rename_keys(__UpperCamelCase	)
			__lowercase			:  Any								=       unflatten_dict(__UpperCamelCase	,				sep='''/'''	)

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

			print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
			pt_model.save_pretrained(__UpperCamelCase	)


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,
			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



