




'''simple docstring'''





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


UpperCamelCase_						:      Union[str, Any]       =						logging.get_logger(__name__)





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=False   )						->			str:

     """simple docstring"""




     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
              " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
              " instructions."   )
          raise

     if not is_sharded:
          _snake_case							=				os.path.abspath(_UpperCamelCase   )
          logger.info(F"""Loading PyTorch weights from {pt_path}"""   )

          _snake_case							=				torch.load(_UpperCamelCase ,						map_location="cpu"   )
          logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."""   )

          _snake_case							=				convert_pytorch_state_dict_to_flax(_UpperCamelCase ,						_UpperCamelCase   )
     else:
          # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
          _snake_case							=				convert_pytorch_sharded_state_dict_to_flax(_UpperCamelCase ,						_UpperCamelCase   )
     return flax_state_dict





def      __a     (       _UpperCamelCase:   Tuple[str] ,						_UpperCamelCase:   np.ndarray ,						_UpperCamelCase:   Dict[str, jnp.ndarray] ,						_UpperCamelCase:   str ,						)						->			(Tuple[str], np.ndarray):

     """simple docstring"""




     def is_key_or_prefix_key_in_dict(_UpperCamelCase:   Tuple[str]   ) -> bool:
          return len(set(_UpperCamelCase   ) & {key, (model_prefix,) + key}   ) > 0

     # layer norm
     _snake_case							=				pt_tuple_key[:-1] + ("scale",)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer mean
     _snake_case							=				pt_tuple_key[:-1] + ("mean",)
     if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer var
     _snake_case							=				pt_tuple_key[:-1] + ("var",)
     if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     _snake_case							=				pt_tuple_key[:-1] + ("embedding",)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     _snake_case							=				pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          _snake_case							=				pt_tensor.transpose(2 ,						3 ,						1 ,						0   )
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     _snake_case							=				pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCamelCase   ):
          _snake_case							=				pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     _snake_case							=				pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     _snake_case							=				pt_tuple_key[:-1] + ("bias",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
     _snake_case							=				None
     if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
          _snake_case							=				pt_tuple_key[-2] + "_g"
     elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
          _snake_case							=				pt_tuple_key[-2] + "_v"
     if name is not None:
          _snake_case							=				pt_tuple_key[:-3] + (name,)
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[Any]   )						->			Any:

     """simple docstring"""




     _snake_case							=				{k: v.numpy() for k, v in pt_state_dict.items()}

     _snake_case							=				flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          _snake_case							=				flax_model.params["params"]
     else:
          _snake_case							=				flax_model.params
     _snake_case							=				flatten_dict(_UpperCamelCase   )

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          _snake_case							=				flatten_dict(flax_model.params["batch_stats"]   )
          random_flax_state_dict.update(_UpperCamelCase   )

     _snake_case							=				{}

     _snake_case							=				(model_prefix not in flax_model_params) and (
         model_prefix in {k.split("."   )[0] for k in pt_state_dict.keys()}
     )
     _snake_case							=				(model_prefix in flax_model_params) and (
         model_prefix not in {k.split("."   )[0] for k in pt_state_dict.keys()}
     )

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          _snake_case							=				tuple(pt_key.split("."   )   )

          # remove base model prefix if necessary
          _snake_case							=				pt_tuple_key[0] == model_prefix
          if load_model_with_head_into_base_model and has_base_model_prefix:
               _snake_case							=				pt_tuple_key[1:]

          # Correctly rename weight parameters
          _snake_case							, _snake_case							=				rename_key_and_reshape_tensor(
              _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )

          # add model prefix if necessary
          _snake_case							=				(model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               _snake_case							=				(model_prefix,) + flax_key

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # add batch stats if the model contains batchnorm layers
          if "batch_stats" in flax_model.params:
               if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                    _snake_case							=				jnp.asarray(_UpperCamelCase   )
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(_UpperCamelCase ,						_UpperCamelCase   )
                    continue

               # also add unexpected weight so that warning is thrown
               _snake_case							=				jnp.asarray(_UpperCamelCase   )

          else:
               # also add unexpected weight so that warning is thrown
               _snake_case							=				jnp.asarray(_UpperCamelCase   )

     return unflatten_dict(_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   int   )						->			Optional[int]:

     """simple docstring"""




     import torch

     # Load the index
     _snake_case							=				{}
     for shard_file in shard_filenames:
          # load using msgpack utils
          _snake_case							=				torch.load(_UpperCamelCase   )
          _snake_case							=				{k: v.numpy() for k, v in pt_state_dict.items()}

          _snake_case							=				flax_model.base_model_prefix

          # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
          if "batch_stats" in flax_model.params:
               _snake_case							=				flax_model.params["params"]

               _snake_case							=				flatten_dict(_UpperCamelCase   )
               random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]   )   )
          else:
               _snake_case							=				flax_model.params
               _snake_case							=				flatten_dict(_UpperCamelCase   )

          _snake_case							=				(model_prefix not in flax_model_params) and (
              model_prefix in {k.split("."   )[0] for k in pt_state_dict.keys()}
          )
          _snake_case							=				(model_prefix in flax_model_params) and (
              model_prefix not in {k.split("."   )[0] for k in pt_state_dict.keys()}
          )
          # Need to change some parameters name to match Flax names
          for pt_key, pt_tensor in pt_state_dict.items():
               _snake_case							=				tuple(pt_key.split("."   )   )

               # remove base model prefix if necessary
               _snake_case							=				pt_tuple_key[0] == model_prefix
               if load_model_with_head_into_base_model and has_base_model_prefix:
                    _snake_case							=				pt_tuple_key[1:]

               # Correctly rename weight parameters
               _snake_case							, _snake_case							=				rename_key_and_reshape_tensor(
                   _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
               # add model prefix if necessary
               _snake_case							=				(model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    _snake_case							=				(model_prefix,) + flax_key

               if flax_key in random_flax_state_dict:
                    if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                         raise ValueError(
                             F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                             F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

            # add batch stats if the model contains batchnorm layers
               if "batch_stats" in flax_model.params:
                    if "mean" in flax_key[-1]:
                         _snake_case							=				jnp.asarray(_UpperCamelCase   )
                         continue
                    if "var" in flax_key[-1]:
                         _snake_case							=				jnp.asarray(_UpperCamelCase   )
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(_UpperCamelCase ,						_UpperCamelCase   )
                         continue

                    # also add unexpected weight so that warning is thrown
                    _snake_case							=				jnp.asarray(_UpperCamelCase   )

               else:
                    # also add unexpected weight so that warning is thrown
                    _snake_case							=				jnp.asarray(_UpperCamelCase   )
     return unflatten_dict(_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   List[str] ,						_UpperCamelCase:   Any   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				os.path.abspath(_UpperCamelCase   )
     logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""   )

     # import correct flax class
     _snake_case							=				getattr(_UpperCamelCase ,						"Flax" + model.__class__.__name__   )

     # load flax weight dict
     with open(_UpperCamelCase ,						"rb"   ) as state_f:
          try:
               _snake_case							=				from_bytes(_UpperCamelCase ,						state_f.read()   )
          except UnpicklingError:
               raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """   )

     return load_flax_weights_in_pytorch_model(_UpperCamelCase ,						_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   str   )						->			List[Any]:

     """simple docstring"""




     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
              " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
              " instructions."   )
          raise

     # check if we have bf16 weights
     _snake_case							=				flatten_dict(jax.tree_util.tree_map(lambda _UpperCamelCase   : x.dtype == jnp.bfloataa ,						_UpperCamelCase   )   ).values()
     if any(_UpperCamelCase   ):
          # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
          # and bf16 is not fully supported in PT yet.
          logger.warning(
              "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
              "before loading those in PyTorch model."   )
          _snake_case							=				jax.tree_util.tree_map(
              lambda _UpperCamelCase   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,						_UpperCamelCase   )

     _snake_case							=				flatten_dict(_UpperCamelCase   )
     _snake_case							=				pt_model.state_dict()

     _snake_case							=				(pt_model.base_model_prefix in flax_state) and (
         pt_model.base_model_prefix not in {k.split("."   )[0] for k in pt_model_dict.keys()}
     )
     _snake_case							=				(pt_model.base_model_prefix not in flax_state) and (
         pt_model.base_model_prefix in {k.split("."   )[0] for k in pt_model_dict.keys()}
     )

     # keep track of unexpected & missing keys
     _snake_case							=				[]
     _snake_case							=				set(pt_model_dict.keys()   )

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          _snake_case							=				flax_key_tuple[0] == pt_model.base_model_prefix
          _snake_case							=				".".join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

          # adapt flax_key to prepare for loading from/to base model only
          if load_model_with_head_into_base_model and has_base_model_prefix:
               _snake_case							=				flax_key_tuple[1:]
          elif load_base_model_into_model_with_head and require_base_model_prefix:
               _snake_case							=				(pt_model.base_model_prefix,) + flax_key_tuple

          # rename flax weights to PyTorch format
          if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCamelCase   ) not in pt_model_dict:
               # conv layer
               _snake_case							=				flax_key_tuple[:-1] + ("weight",)
               _snake_case							=				jnp.transpose(_UpperCamelCase ,						(3, 2, 0, 1)   )
          elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCamelCase   ) not in pt_model_dict:
               # linear layer
               _snake_case							=				flax_key_tuple[:-1] + ("weight",)
               _snake_case							=				flax_tensor.T
          elif flax_key_tuple[-1] in ["scale", "embedding"]:
               _snake_case							=				flax_key_tuple[:-1] + ("weight",)

          # adding batch stats from flax batch norm to pt
          elif "mean" in flax_key_tuple[-1]:
               _snake_case							=				flax_key_tuple[:-1] + ("running_mean",)
          elif "var" in flax_key_tuple[-1]:
               _snake_case							=				flax_key_tuple[:-1] + ("running_var",)

          if "batch_stats" in flax_state:
               _snake_case							=				".".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
          else:
               _snake_case							=				".".join(_UpperCamelCase   )

          # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
          _snake_case							=				{}
          # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
          for key in pt_model_dict:
               _snake_case							=				key.split("."   )
               _snake_case							=				None
               if key_components[-3::2] == ["parametrizations", "original0"]:
                    _snake_case							=				key_components[-2] + "_g"
               elif key_components[-3::2] == ["parametrizations", "original1"]:
                    _snake_case							=				key_components[-2] + "_v"
               if name is not None:
                    _snake_case							=				key_components[:-3] + [name]
                    _snake_case							=				".".join(_UpperCamelCase   )
                    _snake_case							=				key

          if flax_key in special_pt_names:
               _snake_case							=				special_pt_names[flax_key]

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                        F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
               else:
                    # add weight to pytorch dict
                    _snake_case							=				np.asarray(_UpperCamelCase   ) if not isinstance(_UpperCamelCase ,						np.ndarray   ) else flax_tensor
                    _snake_case							=				torch.from_numpy(_UpperCamelCase   )
                    # remove from missing keys
                    missing_keys.remove(_UpperCamelCase   )
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(_UpperCamelCase   )

     pt_model.load_state_dict(_UpperCamelCase   )

     # re-transform missing_keys to list
     _snake_case							=				list(_UpperCamelCase   )

     if len(_UpperCamelCase   ) > 0:
          logger.warning(
              "Some weights of the Flax model were not used when initializing the PyTorch model"
              F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
              F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
              " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
              F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
              " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
              " FlaxBertForSequenceClassification model)."   )
     else:
          logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""   )
     if len(_UpperCamelCase   ) > 0:
          logger.warning(
              F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
              F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
              " use it for predictions and inference."   )
     else:
          logger.warning(
              F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
              "If your task is similar to the task the model of the checkpoint was trained on, "
              F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""   )

     return pt_model








'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_						:      List[str]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase_						:      Optional[Any]       =						250004
UpperCamelCase_						:      Union[str, Any]       =						250020

@require_sentencepiece
@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						MBartTokenizer
     SCREAMING_SNAKE_CASE_							:   List[str]       =						MBartTokenizerFast
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						True
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						True






     def     _lowercase     (			self   )							->      Optional[Any]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )
          tokenizer.save_pretrained(self.tmpdirname   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.tokenize("This is a test"   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,["▁This", "▁is", "▁a", "▁t", "est"]   )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)

          _snake_case							=				tokenizer.tokenize("I was born in 92000, and this is falsé."   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ] ,)
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ] ,)

          _snake_case							=				tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ] ,)






     def     _lowercase     (			self   )							->      List[str]:
          if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

          _snake_case							=				(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                    _snake_case							=				self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
                    _snake_case							=				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )
                         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=True
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it save with the same files
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=False
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )







@require_torch
@require_sentencepiece
@require_tokenizers
class        _a    (						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""facebook/mbart-large-en-ro"""
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						[
         """ UN Chief Says There Is No Military Solution in Syria""",
         """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Dict       =						[
         """Şeful ONU declară că nu există o soluţie militară în Siria""",
         """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
         """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
         """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]






     @classmethod
     def     _lowercase     (			cls   )							->      List[str]:
          _snake_case							=				MBartTokenizer.from_pretrained(
              cls.checkpoint_name ,src_lang="en_XX" ,tgt_lang="ro_RO"   )
          _snake_case							=				1
          return cls






     def     _lowercase     (			self   )							->      Dict:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,250_001   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,250_004   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,250_020   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertIn(_SCREAMING_SNAKE_CASE ,self.tokenizer.all_special_ids   )
          _snake_case							=				[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
          _snake_case							=				self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertNotIn(self.tokenizer.eos_token ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0] ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				10
          _snake_case							=				self.tokenizer(_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE   ).input_ids[0]
          self.assertEqual(ids[-2] ,2   )
          self.assertEqual(ids[-1] ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   ) ,[250_026, 250_001]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				tempfile.mkdtemp()
          _snake_case							=				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_SCREAMING_SNAKE_CASE   )






     @require_torch
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   )
          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






     @require_torch
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.tokenizer(
              self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=len(self.expected_src_tokens   ) ,return_tensors="pt" ,)

          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          self.assertIsInstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          self.assertEqual((2, 14) ,batch.input_ids.shape   )
          self.assertEqual((2, 14) ,batch.attention_mask.shape   )
          _snake_case							=				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(2 ,batch.decoder_input_ids[0, -1]   )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens ,[]   )
          self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id, EN_CODE]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.tokenizer(self.src_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=3 ,return_tensors="pt"   )
          _snake_case							=				self.tokenizer(
              text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=10 ,return_tensors="pt"   )
          _snake_case							=				targets["input_ids"]
          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,self.tokenizer.pad_token_id   )

          self.assertEqual(batch.input_ids.shape[1] ,3   )
          self.assertEqual(batch.decoder_input_ids.shape[1] ,10   )






     @require_torch
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer._build_translation_inputs(
              "A test" ,return_tensors="pt" ,src_lang="en_XX" ,tgt_lang="ar_AR"   )

          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ) ,{
                  # A, test, EOS, en_XX
                  "input_ids": [[62, 3_034, 2, 250_004]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 250_001,
              } ,)


