



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

    __SCREAMING_SNAKE_CASE :	Tuple					 =	r"""(encoder|decoder)\/"""

    if re.match(_lowerCamelCase ,   _lowerCamelCase      ):
      __SCREAMING_SNAKE_CASE :	Optional[int]					 =	re.match(_lowerCamelCase ,   _lowerCamelCase      ).groups()
      if groups[0] == "encoder":
        __SCREAMING_SNAKE_CASE :	Dict					 =	re.sub(r"""/mlp/""" ,   r"""/1/mlp/""" ,   _lowerCamelCase      )
        __SCREAMING_SNAKE_CASE :	int					 =	re.sub(r"""/pre_mlp_layer_norm/""" ,   r"""/1/layer_norm/""" ,   _lowerCamelCase      )

      elif groups[0] == "decoder":
        __SCREAMING_SNAKE_CASE :	str					 =	re.sub(r"""/mlp/""" ,   r"""/2/mlp/""" ,   _lowerCamelCase      )
        __SCREAMING_SNAKE_CASE :	List[Any]					 =	re.sub(r"""/pre_mlp_layer_norm/""" ,   r"""/2/layer_norm/""" ,   _lowerCamelCase      )

        # 2. Convert other classic mappings
    for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
      if old_key in new_key:
        __SCREAMING_SNAKE_CASE :	Tuple					 =	new_key.replace(_lowerCamelCase ,   _lowerCamelCase      )

    print(F"{key} -> {new_key}"      )
    __SCREAMING_SNAKE_CASE :	str					 =	s_dict.pop(_lowerCamelCase      )

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

      s_dict.pop(_lowerCamelCase      )

  return s_dict


UpperCamelCase__ :			List[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        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     Optional[int]      ):
  # Convert a google style config to the hugging face fromat
  import regex as re

  with open(_lowerCamelCase ,   """r"""      ) as f:
    __SCREAMING_SNAKE_CASE :	List[Any]					 =	f.read()

  __SCREAMING_SNAKE_CASE :	Tuple					 =	re.findall(r"""(.*) = ([0-9.]*)""" ,   _lowerCamelCase      )
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{}
  for param, value in regex_match:
    if param in GIN_TO_CONFIG_MAPPING and value != "":
      __SCREAMING_SNAKE_CASE :	Optional[int]					 =	float(_lowerCamelCase      ) if """.""" in value else int(_lowerCamelCase      )

  __SCREAMING_SNAKE_CASE :	List[str]					 =	re.findall(r"""(.*activations) = \(\'(.*)\',\)""" ,   _lowerCamelCase      )[0]
  __SCREAMING_SNAKE_CASE :	int					 =	str(activation[1]      )

  __SCREAMING_SNAKE_CASE :	Any					 =	num_experts
  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwitchTransformersConfig(**_lowerCamelCase      )
  return config


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Dict ,   _lowerCamelCase:     Dict=None ,   _lowerCamelCase:     int="./" ,   _lowerCamelCase:     Tuple=8      ):
  # Initialise PyTorch model

  print(F"Loading flax weights from : {flax_checkpoint_path}"      )
  __SCREAMING_SNAKE_CASE :	Any					 =	checkpoints.load_tax_checkpoint(_lowerCamelCase      )

  if gin_file is not None:
    __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	convert_gin_to_config(_lowerCamelCase ,   _lowerCamelCase      )
  else:
    __SCREAMING_SNAKE_CASE :	Tuple					 =	SwitchTransformersConfig.from_pretrained(_lowerCamelCase      )

  __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwitchTransformersForConditionalGeneration(_lowerCamelCase      )

  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	flax_params["""target"""]
  __SCREAMING_SNAKE_CASE :	Any					 =	flatten_dict(_lowerCamelCase ,   sep="""/"""      )
  __SCREAMING_SNAKE_CASE :	Dict					 =	rename_keys(_lowerCamelCase      )
  __SCREAMING_SNAKE_CASE :	Any					 =	unflatten_dict(_lowerCamelCase ,   sep="""/"""      )

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

  print(F"Save PyTorch model to {pytorch_dump_path}"      )
  pt_model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
      UpperCamelCase__ :			int												=				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,
      )




'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[str]												=				{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	'''t5'''
			_A   :	Optional[Any]    					=	['''past_key_values''']
			_A   :	Any    					=	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=3_2_1_2_8							,  lowerCAmelCase__  :				Dict=5_1_2							,  lowerCAmelCase__  :				str=6_4							,  lowerCAmelCase__  :				Tuple=2_0_4_8							,  lowerCAmelCase__  :				Optional[Any]=6							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Any=8							,  lowerCAmelCase__  :				Dict=3_2							,  lowerCAmelCase__  :				Dict=1_2_8							,  lowerCAmelCase__  :				Any=0.1							,  lowerCAmelCase__  :				str=1E-6							,  lowerCAmelCase__  :				str=1.0							,  lowerCAmelCase__  :				Optional[Any]="relu"							,  lowerCAmelCase__  :				List[str]=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				List[Any]=1							,  **lowerCAmelCase__  :				Dict							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	str					 =	d_model
					__SCREAMING_SNAKE_CASE :	str					 =	d_kv
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	d_ff
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Any					 =	relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE :	int					 =	relative_attention_max_distance
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dropout_rate
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_norm_epsilon
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	initializer_factor
					__SCREAMING_SNAKE_CASE :	Any					 =	feed_forward_proj
					__SCREAMING_SNAKE_CASE :	str					 =	use_cache

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.feed_forward_proj.split("""-"""		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	act_info[-1]
					__SCREAMING_SNAKE_CASE :	int					 =	act_info[0] == """gated"""

					if len(lowerCAmelCase__		) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__		) > 2:
							raise ValueError(
							    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
							    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
							    """'gated-gelu' or 'relu'"""		)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	"""gelu_new"""

					super().__init__(
					    pad_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__							,  )






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""past_encoder_sequence + sequence"""
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch"""}
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase__							,  direction="""inputs"""		)

					return common_inputs



			@property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return 1_3