

"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

UpperCAmelCase													=[8, 5, 9, 7]
UpperCAmelCase													=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCAmelCase													=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,)    ->      None:
          A				  =		claim_vector
          A				  =		allocated_resources_table
          A				  =		maximum_claim_table




    def UpperCamelCase__		( self    )    ->      list[int]:
          return [
              sum(p_item[i] for p_item in self.__allocated_resources_table    )
              for i in range(len(self.__allocated_resources_table[0]    )    )
          ]




    def UpperCamelCase__		( self    )    ->      list[int]:
          return np.array(self.__claim_vector    ) - np.array(
              self.__processes_resource_summation()    )




    def UpperCamelCase__		( self    )    ->      list[list[int]]:
          return [
              list(np.array(self.__maximum_claim_table[i]    ) - np.array(lowerCamelCase_    )    )
              for i, allocated_resource in enumerate(self.__allocated_resources_table    )
          ]




    def UpperCamelCase__		( self    )    ->      dict[int, list[int]]:
          return {self.__need().index(lowerCamelCase_    ): i for i in self.__need()}




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      None:
          A				  =		self.__need()
          A				  =		self.__allocated_resources_table
          A				  =		self.__available_resources()
          A				  =		self.__need_index_manager()
          for kw, val in kwargs.items():
                if kw and val is True:
                      self.__pretty_data()
          print("""_""" * 5_0 + """\n"""    )
          while need_list:
                A				  =		False
                for each_need in need_list:
                      A				  =		True
                      for index, need in enumerate(lowerCamelCase_    ):
                            if need > available_resources[index]:
                                  A				  =		False
                                  break
                      if execution:
                            A				  =		True
                            # get the original index of the process from ind_ctrl db
                            for original_need_index, need_clone in need_index_manager.items():
                                  if each_need == need_clone:
                                        A				  =		original_need_index
                            print(f'Process {process_number + 1} is executing.'    )
                            # remove the process run from stack
                            need_list.remove(lowerCamelCase_    )
                            # update available/freed resources stack
                            A				  =		np.array(lowerCamelCase_    ) + np.array(
                                alloc_resources_table[process_number]    )
                            print(
                                """Updated available resource stack for processes: """
                                + """ """.join([str(lowerCamelCase_    ) for x in available_resources]    )    )
                            break
                if safe:
                      print("""The process is in a safe state.\n"""    )
                else:
                      print("""System in unsafe state. Aborting...\n"""    )
                      break




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          print(""" """ * 9 + """Allocated Resource Table"""    )
          for item in self.__allocated_resources_table:
                print(
                    f'P{self.__allocated_resources_table.index(lowerCamelCase_    ) + 1}'
                    + """ """.join(f'{it:>8}' for it in item    )
                    + """\n"""    )
          print(""" """ * 9 + """System Resource Table"""    )
          for item in self.__maximum_claim_table:
                print(
                    f'P{self.__maximum_claim_table.index(lowerCamelCase_    ) + 1}'
                    + """ """.join(f'{it:>8}' for it in item    )
                    + """\n"""    )
          print(
              """Current Usage by Active Processes: """
              + """ """.join(str(lowerCamelCase_    ) for x in self.__claim_vector    )    )
          print(
              """Initial Available Resources:       """
              + """ """.join(str(lowerCamelCase_    ) for x in self.__available_resources()    )    )
          time.sleep(1    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()







"""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,
  )




