




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   get_tests_dir('fixtures/test_sentencepiece.model')

SCREAMING_SNAKE_CASE__       :      Dict  =   {'target_lang': 'fi', 'source_lang': 'en'}
SCREAMING_SNAKE_CASE__       :      Any  =   '>>zh<<'
SCREAMING_SNAKE_CASE__       :      int  =   'Helsinki-NLP/'

if is_torch_available():
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   'pt'
elif is_tf_available():
       SCREAMING_SNAKE_CASE__       :      str  =   'tf'
else:
       SCREAMING_SNAKE_CASE__       :      Dict  =   'jax'




@require_sentencepiece
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      MarianTokenizer
    lowerCamelCase_      :	Tuple	     =      False
    lowerCamelCase_      :	List[str]	     =      True







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().setUp()
           lowerCamelCase   :      Tuple			 =					["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
           lowerCamelCase   :      Optional[int]			 =					dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__     )     )     )     )
           lowerCamelCase   :      int			 =					Path(self.tmpdirname     )
           save_json(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["vocab"]     )
           save_json(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]     )
           if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                  copyfile(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["source_spm"]     )
                  copyfile(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["target_spm"]     )

           lowerCamelCase   :      Optional[int]			 =					MarianTokenizer.from_pretrained(self.tmpdirname     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      MarianTokenizer:
           return MarianTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           return (
               "This is a test",
               "This is a test",
           )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Dict			 =					"</s>"
           lowerCamelCase   :      str			 =					0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__     )     ,  UpperCamelCase__     )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__     )     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					list(self.get_tokenizer().get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]     ,  "</s>"     )
           self.assertEqual(vocab_keys[1]     ,  "<unk>"     )
           self.assertEqual(vocab_keys[-1]     ,  "<pad>"     )
           self.assertEqual(len(UpperCamelCase__     )     ,  9     )







    def     _lowercase     (    self     )     ->      List[Any]:
           self.assertEqual(self.get_tokenizer().vocab_size     ,  9     )







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Any			 =					MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''     )
           lowerCamelCase   :      Union[str, Any]			 =					en_de_tokenizer(["I am a small frog"]     ,  return_tensors=UpperCamelCase__     )
           self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					[38, 121, 14, 697, 3_8848, 0]
           self.assertListEqual(UpperCamelCase__     ,  batch.input_ids[0]     )

           lowerCamelCase   :      Union[str, Any]			 =					tempfile.mkdtemp()
           en_de_tokenizer.save_pretrained(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					[x.name for x in Path(UpperCamelCase__     ).glob("*"     )]
           self.assertIn("source.spm"     ,  UpperCamelCase__     )
           MarianTokenizer.from_pretrained(UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Dict			 =					self.get_tokenizer()

           lowerCamelCase   :      Optional[Any]			 =					tok(
               ["I am a small frog" * 1000, "I am a small frog"]     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     )
           self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertEqual(batch.input_ids.shape     ,  (2, 512)     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[Any]			 =					self.get_tokenizer()
           lowerCamelCase   :      Any			 =					tok(["I am a tiny frog", "I am a small frog"]     ,  padding=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     )
           self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertEqual(batch_smaller.input_ids.shape     ,  (2, 10)     )







    @slow
    def     _lowercase     (    self     )     ->      List[str]:
           # fmt: off
           lowerCamelCase   :      Dict			 =					{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__     ,  model_name="Helsinki-NLP/opus-mt-en-de"     ,  revision="1a8c2263da11e68e50938f97e10cd57820bd504c"     ,  decode_kwargs={"use_source_tokenizer": True}     ,  )







    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Optional[int]			 =					MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"     )

           lowerCamelCase   :      int			 =					"Tämä on testi"
           lowerCamelCase   :      List[str]			 =					"This is a test"

           lowerCamelCase   :      Optional[int]			 =					[76, 7, 2047, 2]
           lowerCamelCase   :      List[str]			 =					[69, 12, 11, 940, 2]

           lowerCamelCase   :      Optional[int]			 =					tokenizer(UpperCamelCase__     ).input_ids
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      int			 =					tokenizer(text_target=UpperCamelCase__     ).input_ids
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      List[str]			 =					tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__     )









import os

import numpy
import onnx





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    a.name
							snake_case_							=    b.name

							snake_case_							=    ''
							snake_case_							=    ''

							snake_case_							=    a == b

							snake_case_							=    name_a
							snake_case_							=    name_b

							return res





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    list(model.graph.initializer      )
							snake_case_							=    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

														snake_case_							=    inits[i].name
														snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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

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

							snake_case_							=    list(model.graph.initializer      )

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

							snake_case_							=    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__      )

																												snake_case_							=    inits[j].data_type
																												snake_case_							=    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

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

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

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

							snake_case_							=    sorted(UpperCamelCase__      )
							_remove_dup_initializers_from_model(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    'optimized_' + model_file_name
							snake_case_							=    os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )
							onnx.save(UpperCamelCase__   ,							UpperCamelCase__      )

							return new_model




