


'''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


a_  :				List[Any]						= get_tests_dir("""fixtures/test_sentencepiece.model""")

a_  :				List[str]						= {"""target_lang""": """fi""", """source_lang""": """en"""}
a_  :				Any						= """>>zh<<"""
a_  :				List[Any]						= """Helsinki-NLP/"""

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





@require_sentencepiece
class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  MarianTokenizer
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  True






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()
   lowerCamelCase_      =  ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
   lowerCamelCase_      =  dict(zip(UpperCamelCase			,  range(len(UpperCamelCase     )     )     )     )
   lowerCamelCase_      =  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_      =  MarianTokenizer.from_pretrained(self.tmpdirname     )
   tokenizer.save_pretrained(self.tmpdirname     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return MarianTokenizer.from_pretrained(self.tmpdirname			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "</s>"
   lowerCamelCase_      =  0

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   self.assertEqual(self.get_tokenizer().vocab_size			,  9     )






  def   snake_case		(    self     ):







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

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # fmt: off
   lowerCamelCase_      =  {"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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"     )

   lowerCamelCase_      =  "Tämä on testi"
   lowerCamelCase_      =  "This is a test"

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

   lowerCamelCase_      =  tokenizer(UpperCamelCase     ).input_ids
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  tokenizer(text_target=UpperCamelCase     ).input_ids
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  tokenizer.decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase     )
   self.assertEqual(UpperCamelCase			,  UpperCamelCase     )






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

