'''simple docstring'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''simple docstring'''




     for i, input_name in enumerate(node_proto.input      ):
          if input_name == name:
               node_proto.input.insert(snake_case_ ,   snake_case_      )
               node_proto.input.pop(i + 1      )

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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       list(model.graph.initializer      )
     UpperCAmelCase_	   =       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_	   =       inits[i].name
          UpperCAmelCase_	   =       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 ,   snake_case_ ,   snake_case_      )






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

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

     UpperCAmelCase_	   =       0

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

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

                    UpperCAmelCase_	   =       inits[j].data_type
                    UpperCAmelCase_	   =       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: " ,   snake_case_      )
                    total_reduced_size += mem_size

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

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

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

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




