
"""simple docstring"""

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


UpperCamelCase			:       Optional[int]									=				get_tests_dir("fixtures/test_sentencepiece.model")

UpperCamelCase			:       int									=				{"target_lang": "fi", "source_lang": "en"}
UpperCamelCase			:       Union[str, Any]									=				">>zh<<"
UpperCamelCase			:       Tuple									=				"Helsinki-NLP/"

if is_torch_available():
      UpperCamelCase			:       Union[str, Any]									=				"pt"
elif is_tf_available():
      UpperCamelCase			:       List[str]									=				"tf"
else:
      UpperCamelCase			:       Any									=				"jax"

@require_sentencepiece
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		MarianTokenizer
     lowercase		     =		False
     lowercase		     =		True





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().setUp()
      __UpperCamelCase       						= ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
      __UpperCamelCase       						= dict(zip(__UpperCAmelCase			,  range(len(__UpperCAmelCase   )   )   )   )
      __UpperCamelCase       						= 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']   )

      __UpperCamelCase       						= MarianTokenizer.from_pretrained(self.tmpdirname   )
      tokenizer.save_pretrained(self.tmpdirname   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return MarianTokenizer.from_pretrained(self.tmpdirname			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return (
          "This is a test",
          "This is a test",
      )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= '</s>'
      __UpperCamelCase       						= 0

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size			,  9   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= MarianTokenizer.from_pretrained(F'{ORG_NAME}opus-mt-en-de'   )
      __UpperCamelCase       						= en_de_tokenizer(['I am a small frog']			,  return_tensors=__UpperCAmelCase   )
      self.assertIsInstance(__UpperCAmelCase			,  __UpperCAmelCase   )
      __UpperCamelCase       						= [38, 121, 14, 697, 3_8848, 0]
      self.assertListEqual(__UpperCAmelCase			,  batch.input_ids[0]   )

      __UpperCamelCase       						= tempfile.mkdtemp()
      en_de_tokenizer.save_pretrained(__UpperCAmelCase   )
      __UpperCamelCase       						= [x.name for x in Path(__UpperCAmelCase   ).glob('*'   )]
      self.assertIn('source.spm'			,  __UpperCAmelCase   )
      MarianTokenizer.from_pretrained(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_tokenizer()
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {'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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'   )

      __UpperCamelCase       						= 'Tämä on testi'
      __UpperCamelCase       						= 'This is a test'

      __UpperCamelCase       						= [76, 7, 2047, 2]
      __UpperCamelCase       						= [69, 12, 11, 940, 2]

      __UpperCamelCase       						= tokenizer(__UpperCAmelCase   ).input_ids
      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer(text_target=__UpperCAmelCase   ).input_ids
      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer.decode(__UpperCAmelCase			,  skip_special_tokens=__UpperCAmelCase   )
      self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





"""simple docstring"""

import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase			:       Tuple									=				"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCamelCase			:       Dict									=				[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

 with open(os.path.join(snake_case       ,      'utils'       ,      'tf_ops'       ,      'onnx.json'	)	) as f:
  __UpperCamelCase       						= json.load(snake_case	)['opsets']

 for i in range(1       ,      opset + 1	):
  onnx_ops.extend(onnx_opsets[str(snake_case	)]	)

 with open(snake_case       ,      'rb'	) as f:
  saved_model.ParseFromString(f.read()	)

 __UpperCamelCase       						= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node	)

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def	)

    # Convert to list, sorted if you want
 __UpperCamelCase       						= sorted(snake_case	)
 __UpperCamelCase       						= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(snake_case	)

 if strict and len(snake_case	) > 0:
  raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops	)
 elif len(snake_case	) > 0:
  print(f'Found the following incompatible ops for the opset {opset}:'	)
  print(*snake_case       ,      sep='\n'	)
 else:
  print(f'The saved model {saved_model_path} can properly be converted with ONNX.'	)


if __name__ == "__main__":
      UpperCamelCase			:       List[str]									=				argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=1_2, type=int, help="The ONNX opset against which the model has to be tested."
      )
      parser.add_argument(
          "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
      )
      parser.add_argument(
          "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)



