




import os

import numpy
import onnx




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Dict:
   snake_case				:    int           =	a.name
   snake_case				:    Tuple           =	b.name

   snake_case				:    str           =	""""""
   snake_case				:    Optional[int]           =	""""""

   snake_case				:    Optional[int]           =	a == b

   snake_case				:    int           =	name_a
   snake_case				:    List[str]           =	name_b

   return res




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   for i, input_name in enumerate(node_proto.input					):
      if input_name == name:
         node_proto.input.insert(lowercase						,lowercase					)
         node_proto.input.pop(i + 1					)

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   for n in graph_proto.node:
      _node_replace_input_with(lowercase						,lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Optional[Any]           =	list(model.graph.initializer					)
   snake_case				:    Tuple           =	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

      snake_case				:    Union[str, Any]           =	inits[i].name
      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						,lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   snake_case				:    Optional[Any]           =	os.path.dirname(lowercase					)
   snake_case				:    Optional[int]           =	os.path.basename(lowercase					)

   snake_case				:    Optional[Any]           =	onnx.load(os.path.join(lowercase						,lowercase					)					)

   snake_case				:    Optional[Any]           =	list(model.graph.initializer					)

   snake_case				:    Optional[int]           =	set()
   snake_case				:    Optional[int]           =	{}
   snake_case				:    Tuple           =	[]

   snake_case				:    str           =	0

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

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

            snake_case				:    Any           =	inits[j].data_type
            snake_case				:    Dict           =	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: """						,lowercase					)
            total_reduced_size += mem_size

            snake_case				:    Optional[Any]           =	inits[i].name
            snake_case				:    Dict           =	inits[j].name

            if name_i in dup_map:
               dup_map[name_i].append(lowercase					)
            else:
               snake_case				:    Any           =	[name_j]
            ind_to_replace.append((j, i)					)

   print("""total reduced size: """						,total_reduced_size / 1024 / 1024 / 1024						,"""GB"""					)

   snake_case				:    List[str]           =	sorted(lowercase					)
   _remove_dup_initializers_from_model(lowercase						,lowercase						,lowercase					)

   snake_case				:    Optional[int]           =	"""optimized_""" + model_file_name
   snake_case				:    Optional[Any]           =	os.path.join(lowercase						,lowercase					)
   onnx.save(lowercase						,lowercase					)

   return new_model











from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





