

"""simple docstring"""


import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   inspect.getfile(accelerate.test_utils    )
    _lowerCamelCase					=   os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['''scripts''', '''test_cli.py''']    )

    _lowerCamelCase					=   ['''accelerate''', '''launch''']
    _lowerCamelCase					=   Path.home() / '''.cache/huggingface/accelerate'''
    _lowerCamelCase					=   '''default_config.yaml'''
    _lowerCamelCase					=   config_folder / config_file
    _lowerCamelCase					=   config_folder / '''_default_config.yaml'''

    _lowerCamelCase					=   Path('''tests/test_configs'''    )




    @classmethod
    def UpperCamelCase__		( cls    )    ->      Dict:
          if cls.config_path.is_file():
                cls.config_path.rename(cls.changed_path    )




    @classmethod
    def UpperCamelCase__		( cls    )    ->      Optional[int]:
          if cls.changed_path.is_file():
                cls.changed_path.rename(cls.config_path    )




    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.base_cmd
          if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
                cmd += ["--multi_gpu"]
          execute_subprocess_async(cmd + [self.test_file_path] ,env=os.environ.copy()    )




    def UpperCamelCase__		( self    )    ->      Any:
          for config in sorted(self.test_config_path.glob("""**/*.yaml"""    )    ):
                with self.subTest(config_file=lowerCamelCase_    ):
                      execute_subprocess_async(
                          self.base_cmd + ["""--config_file""", str(lowerCamelCase_    ), self.test_file_path] ,env=os.environ.copy()    )




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          execute_subprocess_async(["""accelerate""", """test"""] ,env=os.environ.copy()    )





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''test-tpu'''
    _lowerCamelCase					=   '''us-central1-a'''
    _lowerCamelCase					=   '''ls'''
    _lowerCamelCase					=   ['''accelerate''', '''tpu-config''']
    _lowerCamelCase					=   '''cd /usr/share'''
    _lowerCamelCase					=   '''tests/test_samples/test_command_file.sh'''
    _lowerCamelCase					=   '''Running gcloud compute tpus tpu-vm ssh'''




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		run_command(
              self.cmd
              + ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		run_command(
              self.cmd
              + [
                  """--config_file""",
                  """tests/test_configs/0_12_0.yaml""",
                  """--command""",
                  self.command,
                  """--tpu_zone""",
                  self.tpu_zone,
                  """--tpu_name""",
                  self.tpu_name,
                  """--debug""",
              ] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		run_command(
              self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] ,return_stdout=lowerCamelCase_    )
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		run_command(
              self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		run_command(
              self.cmd
              + [
                  """--config_file""",
                  """tests/test_configs/latest.yaml""",
                  """--command""",
                  self.command,
                  """--command""",
                  """echo \"Hello World\"""",
                  """--debug""",
              ] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		run_command(
              self.cmd
              + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		run_command(
              self.cmd
              + [
                  """--config_file""",
                  """tests/test_configs/0_12_0.yaml""",
                  """--command_file""",
                  self.command_file,
                  """--tpu_zone""",
                  self.tpu_zone,
                  """--tpu_name""",
                  self.tpu_name,
                  """--debug""",
              ] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		run_command(
              self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all' ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		run_command(
              self.cmd
              + [
                  """--config_file""",
                  """tests/test_configs/latest.yaml""",
                  """--install_accelerate""",
                  """--accelerate_version""",
                  """12.0.0""",
                  """--debug""",
              ] ,return_stdout=lowerCamelCase_ ,)
          self.assertIn(
              f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all' ,lowerCamelCase_ ,)







"""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													={
    "/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					(	_a	:						str				):



      """simple docstring"""






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

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

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

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

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      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(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "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					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

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

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

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		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__":
  UpperCAmelCase													=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													=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,
  )




