
import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					Tuple	     =				a.name
       lowercase							:					Dict	     =				b.name

       lowercase							:					List[str]	     =				""""""
       lowercase							:					List[Any]	     =				""""""

       lowercase							:					Dict	     =				a == b

       lowercase							:					Optional[int]	     =				name_a
       lowercase							:					int	     =				name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       for i, input_name in enumerate(node_proto.input    ):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     node_proto.input.pop(i + 1    )

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







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Optional[int]	     =				list(model.graph.initializer    )
       lowercase							:					Dict	     =				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

              lowercase							:					List[Any]	     =				inits[i].name
              lowercase							:					List[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 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







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

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

       lowercase							:					Union[str, Any]	     =				list(model.graph.initializer    )

       lowercase							:					str	     =				set()
       lowercase							:					int	     =				{}
       lowercase							:					Any	     =				[]

       lowercase							:					Tuple	     =				0

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

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

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

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

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

       print("""total reduced size: """ ,     total_reduced_size / 1_024 / 1_024 / 1_024 ,     """GB"""    )

       lowercase							:					Any	     =				sorted(SCREAMING_SNAKE_CASE__    )
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

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

       return new_model


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
