






'''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
_lowerCAmelCase  :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						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       list(s_dict.keys()		)
     for key in keys:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       R'.*/layers_(\d+)'
          SCREAMING_SNAKE_CASE					:			Dict    	=       key
          if re.match(a_       ,      a_		):
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       re.sub(R'layers_(\d+)'       ,      R'block/\1/layer'       ,      a_		)

          SCREAMING_SNAKE_CASE					:			Tuple    	=       R'(encoder|decoder)\/'

          if re.match(a_       ,      a_		):
               SCREAMING_SNAKE_CASE					:			List[str]    	=       re.match(a_       ,      a_		).groups()
               if groups[0] == "encoder":
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       re.sub(R'/mlp/'       ,      R'/1/mlp/'       ,      a_		)
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       re.sub(R'/pre_mlp_layer_norm/'       ,      R'/1/layer_norm/'       ,      a_		)

               elif groups[0] == "decoder":
                    SCREAMING_SNAKE_CASE					:			int    	=       re.sub(R'/mlp/'       ,      R'/2/mlp/'       ,      a_		)
                    SCREAMING_SNAKE_CASE					:			int    	=       re.sub(R'/pre_mlp_layer_norm/'       ,      R'/2/layer_norm/'       ,      a_		)

        # 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					:			Any    	=       new_key.replace(a_       ,      a_		)

          print(f"""{key} -> {new_key}"""		)
          SCREAMING_SNAKE_CASE					:			int    	=       s_dict.pop(a_		)

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

               s_dict.pop(a_		)

     return s_dict


_lowerCAmelCase  :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     __lowerCAmelCase						(       a_       ,      a_		)      ->	int:





     '''simple docstring'''





     import regex as re

     with open(a_       ,      'r'		) as f:
          SCREAMING_SNAKE_CASE					:			Dict    	=       f.read()

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

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

     SCREAMING_SNAKE_CASE					:			List[str]    	=       num_experts
     SCREAMING_SNAKE_CASE					:			int    	=       SwitchTransformersConfig(**a_		)
     return config


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=None       ,      a_="./"       ,      a_=8		)      ->	Any:





     '''simple docstring'''





     print(f"""Loading flax weights from : {flax_checkpoint_path}"""		)
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoints.load_tax_checkpoint(a_		)

     if gin_file is not None:
          SCREAMING_SNAKE_CASE					:			str    	=       convert_gin_to_config(a_       ,      a_		)
     else:
          SCREAMING_SNAKE_CASE					:			str    	=       SwitchTransformersConfig.from_pretrained(a_		)

     SCREAMING_SNAKE_CASE					:			str    	=       SwitchTransformersForConditionalGeneration(a_		)

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       flax_params['target']
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       flatten_dict(a_       ,      sep='/'		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       rename_keys(a_		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       unflatten_dict(a_       ,      sep='/'		)

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

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[Any]  				=    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  :Dict  				=    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'''





import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



