





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

              _lowerCamelCase				:  int						=		r'''(encoder|decoder)\/'''

              if re.match(lowercase_			,     lowercase_   ):
                     _lowerCamelCase				:  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				:  Optional[int]						=		re.sub(r'''/mlp/'''			,     r'''/2/mlp/'''			,     lowercase_   )
                            _lowerCamelCase				:  Dict						=		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				:  Any						=		s_dict.pop(lowercase_   )

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

                     s_dict.pop(lowercase_   )

       return s_dict


_lowerCamelCase      							=   {
    '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(   lowercase_			,     lowercase_   ):
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(lowercase_			,     '''r'''   ) as f:
              _lowerCamelCase				:  Optional[int]						=		f.read()

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

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

       _lowerCamelCase				:  Optional[int]						=		num_experts
       _lowerCamelCase				:  Optional[int]						=		SwitchTransformersConfig(**lowercase_   )
       return config




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

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

       if gin_file is not None:
              _lowerCamelCase				:  Dict						=		convert_gin_to_config(lowercase_			,     lowercase_   )
       else:
              _lowerCamelCase				:  List[Any]						=		SwitchTransformersConfig.from_pretrained(lowercase_   )

       _lowerCamelCase				:  Optional[int]						=		SwitchTransformersForConditionalGeneration(lowercase_   )

       _lowerCamelCase				:  Any						=		flax_params['''target''']
       _lowerCamelCase				:  Any						=		flatten_dict(lowercase_			,     sep='''/'''   )
       _lowerCamelCase				:  Any						=		rename_keys(lowercase_   )
       _lowerCamelCase				:  List[str]						=		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__":
       _lowerCamelCase      							=   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      							=   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,
       )








def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       _lowerCamelCase				:  int						=		(boundary[1] - boundary[0]) / steps
       _lowerCamelCase				:  Any						=		boundary[0]
       _lowerCamelCase				:  Tuple						=		boundary[1]
       _lowerCamelCase				:  Optional[Any]						=		make_points(lowercase_			,     lowercase_			,     lowercase_   )
       _lowerCamelCase				:  Dict						=		0.0
       y += (h / 2.0) * f(lowercase_   )
       for i in x_i:
              # print(i)
              y += h * f(lowercase_   )
       y += (h / 2.0) * f(lowercase_   )
       return y




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Dict						=		a + h
       while x < (b - h):
              yield x
              _lowerCamelCase				:  Dict						=		x + h




def  __UpperCAmelCase(   lowercase_   ):  # enter your function here
       _lowerCamelCase				:  List[str]						=		(x - 0) * (x - 0)
       return y




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  List[Any]						=		0.0  # Lower bound of integration
       _lowerCamelCase				:  Union[str, Any]						=		1.0  # Upper bound of integration
       _lowerCamelCase				:  Dict						=		1_0.0  # define number of steps or resolution
       _lowerCamelCase				:  Optional[int]						=		[a, b]  # define boundary of integration
       _lowerCamelCase				:  str						=		method_a(lowercase_			,     lowercase_   )
       print(F"""y = {y}"""   )


if __name__ == "__main__":
       main()

