




'''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      __a     (       _UpperCamelCase:   Optional[int]   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				list(s_dict.keys()   )
     for key in keys:
          _snake_case							=				r".*/layers_(\d+)"
          _snake_case							=				key
          if re.match(_UpperCamelCase ,						_UpperCamelCase   ):
               _snake_case							=				re.sub(r"layers_(\d+)" ,						r"block/\1/layer" ,						_UpperCamelCase   )

          _snake_case							=				r"(encoder|decoder)\/"

          if re.match(_UpperCamelCase ,						_UpperCamelCase   ):
               _snake_case							=				re.match(_UpperCamelCase ,						_UpperCamelCase   ).groups()
               if groups[0] == "encoder":
                    _snake_case							=				re.sub(r"/mlp/" ,						r"/1/mlp/" ,						_UpperCamelCase   )
                    _snake_case							=				re.sub(r"/pre_mlp_layer_norm/" ,						r"/1/layer_norm/" ,						_UpperCamelCase   )

               elif groups[0] == "decoder":
                    _snake_case							=				re.sub(r"/mlp/" ,						r"/2/mlp/" ,						_UpperCamelCase   )
                    _snake_case							=				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:
                    _snake_case							=				new_key.replace(_UpperCamelCase ,						_UpperCamelCase   )

          print(F"""{key} -> {new_key}"""   )
          _snake_case							=				s_dict.pop(_UpperCamelCase   )

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

               s_dict.pop(_UpperCamelCase   )

     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      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Optional[int]   )						->			str:

     """simple docstring"""




     import regex as re

     with open(_UpperCamelCase ,						"r"   ) as f:
          _snake_case							=				f.read()

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

     _snake_case							=				re.findall(r"(.*activations) = \(\'(.*)\',\)" ,						_UpperCamelCase   )[0]
     _snake_case							=				str(activation[1]   )

     _snake_case							=				num_experts
     _snake_case							=				SwitchTransformersConfig(**_UpperCamelCase   )
     return config





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   Optional[int]="./" ,						_UpperCamelCase:   Optional[int]=8   )						->			Dict:

     """simple docstring"""




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

     if gin_file is not None:
          _snake_case							=				convert_gin_to_config(_UpperCamelCase ,						_UpperCamelCase   )
     else:
          _snake_case							=				SwitchTransformersConfig.from_pretrained(_UpperCamelCase   )

     _snake_case							=				SwitchTransformersForConditionalGeneration(_UpperCamelCase   )

     _snake_case							=				flax_params["target"]
     _snake_case							=				flatten_dict(_UpperCamelCase ,						sep="/"   )
     _snake_case							=				rename_keys(_UpperCamelCase   )
     _snake_case							=				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__":
   UpperCamelCase_						:      List[str]       =						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_						:      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 List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


