

import os

import numpy
import onnx







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       a.name
						SCREAMING_SNAKE_CASE_: int  		=       b.name

						SCREAMING_SNAKE_CASE_: Any  		=       ""
						SCREAMING_SNAKE_CASE_: Tuple  		=       ""

						SCREAMING_SNAKE_CASE_: List[Any]  		=       a == b

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

						return res







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for n in graph_proto.node:
												_node_replace_input_with(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(model.graph.initializer    )
						SCREAMING_SNAKE_CASE_: Optional[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

												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       inits[i].name
												SCREAMING_SNAKE_CASE_: Union[str, 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				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.dirname(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Any  		=       os.path.basename(_UpperCAmelCase    )

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

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

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

						SCREAMING_SNAKE_CASE_: int  		=       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    )

																								SCREAMING_SNAKE_CASE_: List[str]  		=       inits[j].data_type
																								SCREAMING_SNAKE_CASE_: Any  		=       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

																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[i].name
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[j].name

																								if name_i in dup_map:
																														dup_map[name_i].append(_UpperCAmelCase    )
																								else:
																														SCREAMING_SNAKE_CASE_: List[Any]  		=       [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_: List[Any]  		=       sorted(_UpperCAmelCase    )
						_remove_dup_initializers_from_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

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

						return new_model







import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



