



import os

import numpy
import onnx




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    A :       List[str]          =   a.name
    A :       List[Any]          =   b.name

    A :       str          =   """"""
    A :       List[str]          =   """"""

    A :       Dict          =   a == b

    A :       Optional[int]          =   name_a
    A :       Optional[Any]          =   name_b

    return res




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  str:



    """simple docstring"""






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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






    A :       List[str]          =   list(model.graph.initializer		)
    A :       List[str]          =   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 :       Union[str, Any]          =   inits[i].name
        A :       Tuple          =   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 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






    A :       Dict          =   os.path.dirname(lowercase__		)
    A :       List[str]          =   os.path.basename(lowercase__		)

    A :       str          =   onnx.load(os.path.join(lowercase__      ,     lowercase__		)		)

    A :       List[Any]          =   list(model.graph.initializer		)

    A :       Dict          =   set()
    A :       Any          =   {}
    A :       int          =   []

    A :       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__		)

                A :       Optional[int]          =   inits[j].data_type
                A :       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

                A :       Optional[Any]          =   inits[i].name
                A :       str          =   inits[j].name

                if name_i in dup_map:
                    dup_map[name_i].append(lowercase__		)
                else:
                    A :       List[Any]          =   [name_j]
                ind_to_replace.append((j, i)		)

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

    A :       Union[str, Any]          =   sorted(lowercase__		)
    _remove_dup_initializers_from_model(lowercase__      ,     lowercase__      ,     lowercase__		)

    A :       Tuple          =   """optimized_""" + model_file_name
    A :       Union[str, Any]          =   os.path.join(lowercase__      ,     lowercase__		)
    onnx.save(lowercase__      ,     lowercase__		)

    return new_model






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






