




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	:		Union[str, 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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

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

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

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

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

              print(f"""{key} -> {new_key}"""						)
              A__       =    s_dict.pop(lowercase_						)

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

                     s_dict.pop(lowercase_						)

       return s_dict


_lowerCamelCase	:		Dict							       =   {
    """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				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       import regex as re

       with open(lowercase_ ,				'''r'''						) as f:
              A__       =    f.read()

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

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

       A__       =    num_experts
       A__       =    SwitchTransformersConfig(**lowercase_						)
       return config







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_="./" ,				lowercase_=8						)   ->       int:



       """simple docstring"""

       print(f"""Loading flax weights from : {flax_checkpoint_path}"""						)
       A__       =    checkpoints.load_tax_checkpoint(lowercase_						)

       if gin_file is not None:
              A__       =    convert_gin_to_config(lowercase_ ,				lowercase_						)
       else:
              A__       =    SwitchTransformersConfig.from_pretrained(lowercase_						)

       A__       =    SwitchTransformersForConditionalGeneration(lowercase_						)

       A__       =    flax_params['''target''']
       A__       =    flatten_dict(lowercase_ ,				sep='''/'''						)
       A__       =    rename_keys(lowercase_						)
       A__       =    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	:		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""")
  _lowerCamelCase	:		Optional[int]							       =   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,
  )








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


