
"""simple docstring"""
import os

import numpy
import onnx





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							Dict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = a.name
  _SCREAMING_SNAKE_CASE   :				Dict							    = b.name

  _SCREAMING_SNAKE_CASE   :				int							    = ''
  _SCREAMING_SNAKE_CASE   :				Dict							    = ''

  _SCREAMING_SNAKE_CASE   :				Any							    = a == b

  _SCREAMING_SNAKE_CASE   :				List[str]							    = name_a
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = name_b

  return res





def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							str				)  ->    Optional[Any]:
  for i, input_name in enumerate(node_proto.input				):
    if input_name == name:
      node_proto.input.insert(lowerCamelCase_,						lowerCamelCase_				)
      node_proto.input.pop(i + 1				)

  if node_proto.op_type == "If":
    _graph_replace_input_with(node_proto.attribute[0].g,						lowerCamelCase_,						lowerCamelCase_				)
    _graph_replace_input_with(node_proto.attribute[1].g,						lowerCamelCase_,						lowerCamelCase_				)
  if node_proto.op_type == "Loop":
    _graph_replace_input_with(node_proto.attribute[0].g,						lowerCamelCase_,						lowerCamelCase_				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Tuple				)  ->    Optional[int]:
  for n in graph_proto.node:
    _node_replace_input_with(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							List[str]				)  ->    int:
  _SCREAMING_SNAKE_CASE   :				int							    = list(model.graph.initializer				)
  _SCREAMING_SNAKE_CASE   :				int							    = list(model_without_ext.graph.initializer				)
  for i, ref_i in ind_to_replace:
    assert inits_with_data[i].name == inits[i].name
    assert inits_with_data[ref_i].name == inits[ref_i].name
    assert i > ref_i

    _SCREAMING_SNAKE_CASE   :				Tuple							    = inits[i].name
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = inits[ref_i].name

    model_without_ext.graph.initializer.remove(inits[i]				)

    # for n in model.graph.node:
    _graph_replace_input_with(model_without_ext.graph,						lowerCamelCase_,						lowerCamelCase_				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = os.path.dirname(lowerCamelCase_				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = os.path.basename(lowerCamelCase_				)

  _SCREAMING_SNAKE_CASE   :				Any							    = onnx.load(os.path.join(lowerCamelCase_,						lowerCamelCase_				)				)

  _SCREAMING_SNAKE_CASE   :				List[str]							    = list(model.graph.initializer				)

  _SCREAMING_SNAKE_CASE   :				List[str]							    = set()
  _SCREAMING_SNAKE_CASE   :				int							    = {}
  _SCREAMING_SNAKE_CASE   :				int							    = []

  _SCREAMING_SNAKE_CASE   :				Tuple							    = 0

  for i in range(len(lowerCamelCase_				)				):
    if i in dup_set:
      continue

    for j in range(i + 1,						len(lowerCamelCase_				)				):
      if j in dup_set:
        continue
      if _is_equal_tensor_proto(inits[i],						inits[j]				):
        dup_set.add(lowerCamelCase_				)
        dup_set.add(lowerCamelCase_				)

        _SCREAMING_SNAKE_CASE   :				Any							    = inits[j].data_type
        _SCREAMING_SNAKE_CASE   :				List[str]							    = numpy.prod(inits[j].dims				)
        if dtype == 1:
          mem_size *= 4
        elif dtype == 6:
          mem_size *= 4
        elif dtype == 7 or dtype == 1_1:
          mem_size *= 8
        else:
          print("unexpected data type: ",						lowerCamelCase_				)
        total_reduced_size += mem_size

        _SCREAMING_SNAKE_CASE   :				Tuple							    = inits[i].name
        _SCREAMING_SNAKE_CASE   :				int							    = inits[j].name

        if name_i in dup_map:
          dup_map[name_i].append(lowerCamelCase_				)
        else:
          _SCREAMING_SNAKE_CASE   :				List[str]							    = [name_j]
        ind_to_replace.append((j, i)				)

  print("total reduced size: ",						total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4,						"GB"				)

  _SCREAMING_SNAKE_CASE   :				List[str]							    = sorted(lowerCamelCase_				)
  _remove_dup_initializers_from_model(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_				)

  _SCREAMING_SNAKE_CASE   :				Any							    = 'optimized_' + model_file_name
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = os.path.join(lowerCamelCase_,						lowerCamelCase_				)
  onnx.save(lowerCamelCase_,						lowerCamelCase_				)

  return new_model





"""simple docstring"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



