




import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  )       ->  Optional[Any]:







			"""simple docstring"""





			return EnvironmentCommand()





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  )       ->  Optional[int]:







			"""simple docstring"""





			return EnvironmentCommand(args.accelerate_config_file  )







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				@staticmethod
				def 		snake_case	(			snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      parser.add_parser('env' )
							download_parser.set_defaults(func=snake_case__ )
							download_parser.add_argument(
							    '--accelerate-config_file'      ,default=snake_case__      ,help='The accelerate config file to use for the default values in the launching script.'      ,)
							download_parser.set_defaults(func=snake_case__ )






				def __init__(			self      ,snake_case__      ,*snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      accelerate_config_file






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      'not installed'
							if is_safetensors_available():
										import safetensors

										SCREAMING_SNAKE_CASE_   : str					  =      safetensors.__version__
							elif importlib.util.find_spec('safetensors' ) is not None:
										import safetensors

										SCREAMING_SNAKE_CASE_   : int					  =      F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

							SCREAMING_SNAKE_CASE_   : int					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : Tuple					  =      'not found'
							if is_accelerate_available():
										import accelerate
										from accelerate.commands.config import default_config_file, load_config_from_file

										SCREAMING_SNAKE_CASE_   : Any					  =      accelerate.__version__
										# Get the default from the config file.
										if self._accelerate_config_file is not None or os.path.isfile(snake_case__ ):
													SCREAMING_SNAKE_CASE_   : Dict					  =      load_config_from_file(self._accelerate_config_file ).to_dict()

										SCREAMING_SNAKE_CASE_   : int					  =      (
										    '\n'.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] )
										    if isinstance(snake_case__      ,snake_case__ )
										    else F'\t{accelerate_config}'
										)

							SCREAMING_SNAKE_CASE_   : str					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : str					  =      'NA'
							if is_torch_available():
										import torch

										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.__version__
										SCREAMING_SNAKE_CASE_   : Dict					  =      torch.cuda.is_available()

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'NA'
							if is_tf_available():
										import tensorflow as tf

										SCREAMING_SNAKE_CASE_   : Any					  =      tf.__version__
										try:
													# deprecated in v2.1
													SCREAMING_SNAKE_CASE_   : Any					  =      tf.test.is_gpu_available()
										except AttributeError:
													# returns list of devices, convert to bool
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      bool(tf.config.list_physical_devices('GPU' ) )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : Tuple					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'not installed'
							SCREAMING_SNAKE_CASE_   : str					  =      'NA'
							if is_flax_available():
										import flax
										import jax
										import jaxlib

										SCREAMING_SNAKE_CASE_   : List[Any]					  =      flax.__version__
										SCREAMING_SNAKE_CASE_   : Tuple					  =      jax.__version__
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      jaxlib.__version__
										SCREAMING_SNAKE_CASE_   : Tuple					  =      jax.lib.xla_bridge.get_backend().platform

							SCREAMING_SNAKE_CASE_   : List[str]					  =      {
							    '`transformers` version': version,
							    'Platform': platform.platform(),
							    'Python version': platform.python_version(),
							    'Huggingface_hub version': huggingface_hub.__version__,
							    'Safetensors version': F'{safetensors_version}',
							    'Accelerate version': F'{accelerate_version}',
							    'Accelerate config': F'{accelerate_config_str}',
							    'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
							    'Tensorflow version (GPU?)': F'{tf_version} ({tf_cuda_available})',
							    'Flax version (CPU?/GPU?/TPU?)': F'{flax_version} ({jax_backend})',
							    'Jax version': F'{jax_version}',
							    'JaxLib version': F'{jaxlib_version}',
							    'Using GPU in script?': '<fill in>',
							    'Using distributed or parallel set-up in script?': '<fill in>',
							}

							print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n' )
							print(self.format_dict(snake_case__ ) )

							return info






				@staticmethod
				def 		snake_case	(			snake_case__ ):
							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()] ) + "\n"









import os

import numpy
import onnx





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      a.name
			SCREAMING_SNAKE_CASE_   : Dict					  =      b.name

			SCREAMING_SNAKE_CASE_   : str					  =      ''
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ''

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      a == b

			SCREAMING_SNAKE_CASE_   : str					  =      name_a
			SCREAMING_SNAKE_CASE_   : str					  =      name_b

			return res





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Dict  )       ->  Optional[int]:







			"""simple docstring"""





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

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  List[Any]:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Any  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      list(model.graph.initializer  )
			SCREAMING_SNAKE_CASE_   : 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

						SCREAMING_SNAKE_CASE_   : List[str]					  =      inits[i].name
						SCREAMING_SNAKE_CASE_   : str					  =      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  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.dirname(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.basename(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : int					  =      onnx.load(os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )  )

			SCREAMING_SNAKE_CASE_   : Any					  =      list(model.graph.initializer  )

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

			SCREAMING_SNAKE_CASE_   : int					  =      0

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

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

												SCREAMING_SNAKE_CASE_   : int					  =      inits[j].data_type
												SCREAMING_SNAKE_CASE_   : 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: '  ,							lowerCamelCase_  )
												total_reduced_size += mem_size

												SCREAMING_SNAKE_CASE_   : int					  =      inits[i].name
												SCREAMING_SNAKE_CASE_   : List[Any]					  =      inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(lowerCamelCase_  )
												else:
															SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [name_j]
												ind_to_replace.append((j, i)  )

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

			SCREAMING_SNAKE_CASE_   : int					  =      sorted(lowerCamelCase_  )
			_remove_dup_initializers_from_model(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'optimized_' + model_file_name
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )
			onnx.save(lowerCamelCase_  ,							lowerCamelCase_  )

			return new_model



