


"""simple docstring"""

import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def lowerCAmelCase_   (					UpperCamelCase__						:			Union[dict, list, tuple, torch.Tensor]			):






 """simple docstring"""







 __lowercase						=						[]
 if isinstance(UpperCamelCase__					, UpperCamelCase__			):
  for v in tree.values():
   shapes.extend(_fetch_dims(UpperCamelCase__			)			)
 elif isinstance(UpperCamelCase__					, (list, tuple)			):
  for t in tree:
   shapes.extend(_fetch_dims(UpperCamelCase__			)			)
 elif isinstance(UpperCamelCase__					, torch.Tensor			):
  shapes.append(tree.shape			)
 else:
  raise ValueError("""Not supported"""			)

 return shapes


@torch.jit.ignore
def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Tuple[int, ...]			):






 """simple docstring"""







 __lowercase						=						[]
 for d in reversed(UpperCamelCase__			):
  idx.append(flat_idx % d			)
  __lowercase						=						flat_idx // d

 return tuple(reversed(UpperCamelCase__			)			)


@torch.jit.ignore
def lowerCAmelCase_   (					UpperCamelCase__						:			Sequence[int]					, UpperCamelCase__						:			Sequence[int]					, UpperCamelCase__						:			Sequence[int]					, UpperCamelCase__						:			Optional[Sequence[bool]] = None					, UpperCamelCase__						:			Optional[Sequence[bool]] = None					, ):






 """simple docstring"""







 def reduce_edge_list(UpperCamelCase__						:			List[bool]			) -> None:
  __lowercase						=						True
  for i in range(len(UpperCamelCase__			)			):
   __lowercase						=						-1 * (i + 1)
   l[reversed_idx] &= tally
   __lowercase						=						l[reversed_idx]

 if start_edges is None:
  __lowercase						=						[s == 0 for s in start]
  reduce_edge_list(UpperCamelCase__			)
 if end_edges is None:
  __lowercase						=						[e == (d - 1) for e, d in zip(UpperCamelCase__					, UpperCamelCase__			)]
  reduce_edge_list(UpperCamelCase__			)

 # Base cases. Either start/end are empty and we're done, or the final,
 # one-dimensional tensor can be simply sliced
 if len(UpperCamelCase__			) == 0:
  return [()]
 elif len(UpperCamelCase__			) == 1:
  return [(slice(start[0]					, end[0] + 1			),)]

 __lowercase						=						[]
 __lowercase						=						[]

 # Dimensions common to start and end can be selected directly
 for s, e in zip(UpperCamelCase__					, UpperCamelCase__			):
  if s == e:
   path_list.append(slice(UpperCamelCase__					, s + 1			)			)
  else:
   break

 __lowercase						=						tuple(UpperCamelCase__			)
 __lowercase						=						len(UpperCamelCase__			)

 # start == end, and we're done
 if divergence_idx == len(UpperCamelCase__			):
  return [path]

 def upper() -> Tuple[Tuple[slice, ...], ...]:
  assert start_edges is not None
  assert end_edges is not None

  __lowercase						=						start[divergence_idx]
  return tuple(
      path + (slice(UpperCamelCase__					, sdi + 1			),) + s
      for s in _get_minimal_slice_set(
          start[divergence_idx + 1 :]					, [d - 1 for d in dims[divergence_idx + 1 :]]					, dims[divergence_idx + 1 :]					, start_edges=start_edges[divergence_idx + 1 :]					, end_edges=[True for _ in end_edges[divergence_idx + 1 :]]					, )			)

 def lower() -> Tuple[Tuple[slice, ...], ...]:
  assert start_edges is not None
  assert end_edges is not None

  __lowercase						=						end[divergence_idx]
  return tuple(
      path + (slice(UpperCamelCase__					, edi + 1			),) + s
      for s in _get_minimal_slice_set(
          [0 for _ in start[divergence_idx + 1 :]]					, end[divergence_idx + 1 :]					, dims[divergence_idx + 1 :]					, start_edges=[True for _ in start_edges[divergence_idx + 1 :]]					, end_edges=end_edges[divergence_idx + 1 :]					, )			)

 # If both start and end are at the edges of the subtree rooted at
 # divergence_idx, we can just select the whole subtree at once
 if start_edges[divergence_idx] and end_edges[divergence_idx]:
  slices.append(path + (slice(start[divergence_idx]					, end[divergence_idx] + 1			),)			)
 # If just start is at the edge, we can grab almost all of the subtree,
 # treating only the ragged bottom edge as an edge case
 elif start_edges[divergence_idx]:
  slices.append(path + (slice(start[divergence_idx]					, end[divergence_idx]			),)			)
  slices.extend(lower()			)
 # Analogous to the previous case, but the top is ragged this time
 elif end_edges[divergence_idx]:
  slices.extend(upper()			)
  slices.append(path + (slice(start[divergence_idx] + 1					, end[divergence_idx] + 1			),)			)
 # If both sides of the range are ragged, we need to handle both sides
 # separately. If there's contiguous meat in between them, we can index it
 # in one big chunk
 else:
  slices.extend(upper()			)
  __lowercase						=						end[divergence_idx] - start[divergence_idx]
  if middle_ground > 1:
   slices.append(path + (slice(start[divergence_idx] + 1					, end[divergence_idx]			),)			)
  slices.extend(lower()			)

 return slices


@torch.jit.ignore
def lowerCAmelCase_   (					UpperCamelCase__						:			torch.Tensor					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						t.shape[:no_batch_dims]
 __lowercase						=						list(_flat_idx_to_idx(UpperCamelCase__					, UpperCamelCase__			)			)
 # _get_minimal_slice_set is inclusive
 __lowercase						=						list(_flat_idx_to_idx(flat_end - 1					, UpperCamelCase__			)			)

 # Get an ordered list of slices to perform
 __lowercase						=						_get_minimal_slice_set(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, )

 __lowercase						=						[t[s] for s in slices]

 return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]			) for s in sliced_tensors]			)


def lowerCAmelCase_   (					UpperCamelCase__						:			Callable					, UpperCamelCase__						:			Dict[str, Any]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			bool = False					, UpperCamelCase__						:			Any = None					, UpperCamelCase__						:			bool = False					, ):






 """simple docstring"""







 if not (len(UpperCamelCase__			) > 0):
  raise ValueError("""Must provide at least one input"""			)

 __lowercase						=						[shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__			)]
 __lowercase						=						tuple([max(UpperCamelCase__			) for s in zip(*UpperCamelCase__			)]			)

 def _prep_inputs(UpperCamelCase__						:			torch.Tensor			) -> torch.Tensor:
  if not low_mem:
   if not sum(t.shape[:no_batch_dims]			) == no_batch_dims:
    __lowercase						=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]			)
   __lowercase						=						t.reshape(-1					, *t.shape[no_batch_dims:]			)
  else:
   __lowercase						=						t.expand(orig_batch_dims + t.shape[no_batch_dims:]			)
  return t

 __lowercase						=						tensor_tree_map(_prep_inputs					, UpperCamelCase__			)
 __lowercase						=						None
 if _out is not None:
  __lowercase						=						tensor_tree_map(lambda UpperCamelCase__			: t.view([-1] + list(t.shape[no_batch_dims:]			)			)					, _out			)

 __lowercase						=						1
 for d in orig_batch_dims:
  flat_batch_dim *= d

 __lowercase						=						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

 def _select_chunk(UpperCamelCase__						:			torch.Tensor			) -> torch.Tensor:
  return t[i : i + chunk_size] if t.shape[0] != 1 else t

 __lowercase						=						0
 __lowercase						=						prepped_outputs
 for _ in range(UpperCamelCase__			):
  # Chunk the input
  if not low_mem:
   __lowercase						=						_select_chunk
  else:
   __lowercase						=						partial(
       _chunk_slice					, flat_start=UpperCamelCase__					, flat_end=min(UpperCamelCase__					, i + chunk_size			)					, no_batch_dims=len(UpperCamelCase__			)					, )

  __lowercase						=						tensor_tree_map(UpperCamelCase__					, UpperCamelCase__			)

  # Run the layer on the chunk
  __lowercase						=						layer(**UpperCamelCase__			)

  # Allocate space for the output
  if out is None:
   __lowercase						=						tensor_tree_map(lambda UpperCamelCase__			: t.new_zeros((flat_batch_dim,) + t.shape[1:]			)					, UpperCamelCase__			)

  # Put the chunk in its pre-allocated space
  if isinstance(UpperCamelCase__					, UpperCamelCase__			):

   def assign(UpperCamelCase__						:			dict					, UpperCamelCase__						:			dict			) -> None:
    for k, v in da.items():
     if isinstance(UpperCamelCase__					, UpperCamelCase__			):
      assign(UpperCamelCase__					, da[k]			)
     else:
      if _add_into_out:
       v[i : i + chunk_size] += da[k]
      else:
       __lowercase						=						da[k]

   assign(UpperCamelCase__					, UpperCamelCase__			)
  elif isinstance(UpperCamelCase__					, UpperCamelCase__			):
   for xa, xa in zip(UpperCamelCase__					, UpperCamelCase__			):
    if _add_into_out:
     xa[i : i + chunk_size] += xa
    else:
     __lowercase						=						xa
  elif isinstance(UpperCamelCase__					, torch.Tensor			):
   if _add_into_out:
    out[i : i + chunk_size] += output_chunk
   else:
    __lowercase						=						output_chunk
  else:
   raise ValueError("""Not supported"""			)

  i += chunk_size

 __lowercase						=						tensor_tree_map(lambda UpperCamelCase__			: t.view(orig_batch_dims + t.shape[1:]			)					, UpperCamelCase__			)

 return out





class  lowerCamelCase__    :
      def __init__(		self						:						Optional[int]   ,							A_						:						int = 5_1_2   ,							):






       '''simple docstring'''


       __lowercase						=						max_chunk_size
       __lowercase						=						None
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Callable   ,							A_						:						tuple   ,							A_						:						int				):






       '''simple docstring'''


       logging.info("""Tuning chunk size..."""				)

       if min_chunk_size >= self.max_chunk_size:
        return min_chunk_size

       __lowercase						=						[2**l for l in range(int(math.log(self.max_chunk_size   ,							2				)				) + 1				)]
       __lowercase						=						[c for c in candidates if c > min_chunk_size]
       __lowercase						=						[min_chunk_size] + candidates
       candidates[-1] += 4

       def test_chunk_size(A_						:						int				) -> bool:
        try:
         with torch.no_grad():
          fn(*A_   ,							chunk_size=A_				)
         return True
        except RuntimeError:
         return False

       __lowercase						=						0
       __lowercase						=						len(A_				) - 1
       while i > min_viable_chunk_size_index:
        __lowercase						=						test_chunk_size(candidates[i]				)
        if not viable:
         __lowercase						=						(min_viable_chunk_size_index + i) // 2
        else:
         __lowercase						=						i
         __lowercase						=						(i + len(A_				) - 1) // 2

       return candidates[min_viable_chunk_size_index]
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Iterable   ,							A_						:						Iterable				):






       '''simple docstring'''


       __lowercase						=						True
       for aa, aa in zip(A_   ,							A_				):
        assert type(A_				) == type(A_				)
        if isinstance(A_   ,							(list, tuple)				):
         consistent &= self._compare_arg_caches(A_   ,							A_				)
        elif isinstance(A_   ,							A_				):
         __lowercase						=						[v for _, v in sorted(aa.items()   ,							key=lambda A_				: x[0]				)]
         __lowercase						=						[v for _, v in sorted(aa.items()   ,							key=lambda A_				: x[0]				)]
         consistent &= self._compare_arg_caches(A_   ,							A_				)
        else:
         consistent &= aa == aa

       return consistent






      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Callable   ,							A_						:						tuple   ,							A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						True
       __lowercase						=						tree_map(lambda A_				: a.shape if isinstance(A_   ,							torch.Tensor				) else a   ,							A_   ,							A_				)
       if self.cached_arg_data is not None:
        # If args have changed shape/value, we need to re-tune
        assert len(self.cached_arg_data				) == len(A_				)
        __lowercase						=						self._compare_arg_caches(self.cached_arg_data   ,							A_				)
       else:
        # Otherwise, we can reuse the precomputed value
        __lowercase						=						False

       if not consistent:
        __lowercase						=						self._determine_favorable_chunk_size(
            A_   ,							A_   ,							A_   ,							)
        __lowercase						=						arg_data

       assert self.cached_chunk_size is not None

       return self.cached_chunk_size




"""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__					=get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__					={"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__					=">>zh<<"
UpperCAmelCase__					="Helsinki-NLP/"

if is_torch_available():
       UpperCAmelCase__					="pt"
elif is_tf_available():
       UpperCAmelCase__					="tf"
else:
       UpperCAmelCase__					="jax"





@require_sentencepiece
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Dict           =		MarianTokenizer
      a			:  Optional[int]           =		False
      a			:  Any           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       super().setUp()
       __lowercase						=						["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase						=						dict(zip(A_   ,							range(len(A_				)				)				)				)
       __lowercase						=						Path(self.tmpdirname				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

       __lowercase						=						MarianTokenizer.from_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							**A_						:						Any				):






       '''simple docstring'''


       return MarianTokenizer.from_pretrained(self.tmpdirname   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						"""</s>"""
       __lowercase						=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						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(A_				)   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''				)
       __lowercase						=						en_de_tokenizer(["""I am a small frog"""]   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       __lowercase						=						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(A_   ,							batch.input_ids[0]				)

       __lowercase						=						tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_				)
       __lowercase						=						[x.name for x in Path(A_				).glob("""*"""				)]
       self.assertIn("""source.spm"""   ,							A_				)
       MarianTokenizer.from_pretrained(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()

       __lowercase						=						tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]   ,							padding=A_   ,							truncation=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch.input_ids.shape   ,							(2, 5_1_2)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()
       __lowercase						=						tok(["""I am a tiny frog""", """I am a small frog"""]   ,							padding=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch_smaller.input_ids.shape   ,							(2, 1_0)				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """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=A_   ,							model_name="""Helsinki-NLP/opus-mt-en-de"""   ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,							decode_kwargs={"""use_source_tokenizer""": True}   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


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

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

       __lowercase						=						[7_6, 7, 2_0_4_7, 2]
       __lowercase						=						[6_9, 1_2, 1_1, 9_4_0, 2]

       __lowercase						=						tokenizer(A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer(text_target=A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.decode(A_   ,							skip_special_tokens=A_				)
       self.assertEqual(A_   ,							A_				)
