





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
     from ...onnx.config import PatchingSpec
     from ...tokenization_utils_base import PreTrainedTokenizerBase


_UpperCamelCase				=       logging.get_logger(__name__)

_UpperCamelCase				=       {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}




class  _lowerCamelCase       (			a_			):



      """simple docstring"""






      UpperCAmelCase_  :					Any      			="longformer"







      def __init__(       self ,  UpperCAmelCase = 512 ,  UpperCAmelCase = 2 ,  UpperCAmelCase = 1 ,  UpperCAmelCase = 0 ,  UpperCAmelCase = 2 ,  UpperCAmelCase = 30522 ,  UpperCAmelCase = 768 ,  UpperCAmelCase = 12 ,  UpperCAmelCase = 12 ,  UpperCAmelCase = 3072 ,  UpperCAmelCase = "gelu" ,  UpperCAmelCase = 0.1 ,  UpperCAmelCase = 0.1 ,  UpperCAmelCase = 512 ,  UpperCAmelCase = 2 ,  UpperCAmelCase = 0.02 ,  UpperCAmelCase = 1E-12 ,  UpperCAmelCase = False ,  **UpperCAmelCase ,  )				->      Optional[Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=_A ,  **_A      )

        __snake_case      : Dict						=				attention_window
        __snake_case      : Tuple						=				sep_token_id
        __snake_case      : Tuple						=				bos_token_id
        __snake_case      : List[Any]						=				eos_token_id
        __snake_case      : Optional[int]						=				vocab_size
        __snake_case      : int						=				hidden_size
        __snake_case      : str						=				num_hidden_layers
        __snake_case      : Optional[Any]						=				num_attention_heads
        __snake_case      : Optional[Any]						=				hidden_act
        __snake_case      : int						=				intermediate_size
        __snake_case      : Tuple						=				hidden_dropout_prob
        __snake_case      : Tuple						=				attention_probs_dropout_prob
        __snake_case      : List[Any]						=				max_position_embeddings
        __snake_case      : Optional[Any]						=				type_vocab_size
        __snake_case      : List[Any]						=				initializer_range
        __snake_case      : Dict						=				layer_norm_eps
        __snake_case      : Optional[int]						=				onnx_export




class  _lowerCamelCase       (			a_			):



      """simple docstring"""






      def __init__(       self ,  UpperCAmelCase ,  UpperCAmelCase = "default" ,  UpperCAmelCase = None      )				->      Union[str, Any]:






        '''simple docstring'''






        super().__init__(_A ,  _A ,  _A      )
        __snake_case      : List[str]						=				True







      @property
      def   UpperCAmelCase						(       self      )				->      str:






        '''simple docstring'''






        if self.task == "multiple-choice":
          __snake_case      : List[Any]						=				{0: "batch", 1: "choice", 2: "sequence"}
        else:
          __snake_case      : Tuple						=				{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("global_attention_mask", dynamic_axis),
            ]      )







      @property
      def   UpperCAmelCase						(       self      )				->      Dict:






        '''simple docstring'''






        __snake_case      : Optional[Any]						=				super().outputs
        if self.task == "default":
          __snake_case      : int						=				{0: "batch"}
        return outputs







      @property
      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        return 1E-4







      @property
      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        return max(super().default_onnx_opset ,  14      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase = -1 ,  UpperCAmelCase = -1 ,  UpperCAmelCase = False ,  UpperCAmelCase = None ,  )				->      Dict:






        '''simple docstring'''






        __snake_case      : int						=				super().generate_dummy_inputs(
            preprocessor=_A ,  batch_size=_A ,  seq_length=_A ,  is_pair=_A ,  framework=_A      )
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        __snake_case      : List[str]						=				torch.zeros_like(inputs["input_ids"]      )
        # make every second token global
        __snake_case      : Optional[Any]						=				1

        return inputs








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				:    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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

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

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

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

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

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

			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(UpperCamelCase__ ):
												UpperCAmelCase   		=				expert_weihts[idx]
												print(F"""{key} -> {key.replace("expert/"			,			"nested fstring" )}""" )

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "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  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

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

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

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				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				:    Tuple       =   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				:    Tuple       =   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,
				)



