



import os

import numpy
import onnx


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		a.name
 a						=		b.name

 a						=		''''''
 a						=		''''''

 a						=		a == b

 a						=		name_a
 a						=		name_b

 return res


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 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 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 for n in graph_proto.node:
  _node_replace_input_with(lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :str ,					UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 a						=		list(model.graph.initializer					)
 a						=		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

  a						=		inits[i].name
  a						=		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 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		os.path.dirname(lowerCAmelCase_					)
 a						=		os.path.basename(lowerCAmelCase_					)

 a						=		onnx.load(os.path.join(lowerCAmelCase_ ,					lowerCAmelCase_					)					)

 a						=		list(model.graph.initializer					)

 a						=		set()
 a						=		{}
 a						=		[]

 a						=		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_					)

    a						=		inits[j].data_type
    a						=		numpy.prod(inits[j].dims					)
    if dtype == 1:
     mem_size *= 4
    elif dtype == 6:
     mem_size *= 4
    elif dtype == 7 or dtype == 11:
     mem_size *= 8
    else:
     print("unexpected data type: " ,					lowerCAmelCase_					)
    total_reduced_size += mem_size

    a						=		inits[i].name
    a						=		inits[j].name

    if name_i in dup_map:
     dup_map[name_i].append(lowerCAmelCase_					)
    else:
     a						=		[name_j]
    ind_to_replace.append((j, i)					)

 print("total reduced size: " ,					total_reduced_size / 10_24 / 10_24 / 10_24 ,					"GB"					)

 a						=		sorted(lowerCAmelCase_					)
 _remove_dup_initializers_from_model(lowerCAmelCase_ ,					lowerCAmelCase_ ,					lowerCAmelCase_					)

 a						=		'''optimized_''' + model_file_name
 a						=		os.path.join(lowerCAmelCase_ ,					lowerCAmelCase_					)
 onnx.save(lowerCAmelCase_ ,					lowerCAmelCase_					)

 return new_model






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

