





import os

import numpy
import onnx




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Tuple   )   ->			List[Any]:
    UpperCamelCase__							:       str      =   a.name
    UpperCamelCase__							:       str      =   b.name

    UpperCamelCase__							:       Tuple      =   ''''''
    UpperCamelCase__							:       Optional[Any]      =   ''''''

    UpperCamelCase__							:       str      =   a == b

    UpperCamelCase__							:       int      =   name_a
    UpperCamelCase__							:       Any      =   name_b

    return res




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]   )   ->			Tuple:
    for i, input_name in enumerate(node_proto.input   ):
        if input_name == name:
            node_proto.input.insert(__UpperCAmelCase	,     __UpperCAmelCase   )
            node_proto.input.pop(i + 1   )

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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    Dict   )   ->			Tuple:
    for n in graph_proto.node:
        _node_replace_input_with(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Tuple   )   ->			int:
    UpperCamelCase__							:       Union[str, Any]      =   list(model.graph.initializer   )
    UpperCamelCase__							:       Union[str, Any]      =   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

        UpperCamelCase__							:       List[Any]      =   inits[i].name
        UpperCamelCase__							:       Optional[int]      =   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	,     __UpperCAmelCase	,     __UpperCAmelCase   )





def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any   )   ->			List[str]:
    UpperCamelCase__							:       Any      =   os.path.dirname(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   os.path.basename(__UpperCAmelCase   )

    UpperCamelCase__							:       Tuple      =   onnx.load(os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )   )

    UpperCamelCase__							:       int      =   list(model.graph.initializer   )

    UpperCamelCase__							:       Union[str, Any]      =   set()
    UpperCamelCase__							:       Optional[int]      =   {}
    UpperCamelCase__							:       Any      =   []

    UpperCamelCase__							:       Tuple      =   0

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

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

                UpperCamelCase__							:       Optional[Any]      =   inits[j].data_type
                UpperCamelCase__							:       List[str]      =   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: '''	,     __UpperCAmelCase   )
                total_reduced_size += mem_size

                UpperCamelCase__							:       int      =   inits[i].name
                UpperCamelCase__							:       int      =   inits[j].name

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

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

    UpperCamelCase__							:       str      =   sorted(__UpperCAmelCase   )
    _remove_dup_initializers_from_model(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    UpperCamelCase__							:       List[Any]      =   '''optimized_''' + model_file_name
    UpperCamelCase__							:       Tuple      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )
    onnx.save(__UpperCAmelCase	,     __UpperCAmelCase   )

    return new_model








import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

