






'''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__ =			logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=False							):


   """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:
      lowercase_	:    Optional[Any]           =     os.path.abspath(UpperCamelCase__							)
      logger.info(F"""Loading PyTorch weights from {pt_path}"""							)

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

      lowercase_	:    List[str]           =     convert_pytorch_state_dict_to_flax(UpperCamelCase__						,		UpperCamelCase__							)
   else:
      # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
      lowercase_	:    Optional[Any]           =     convert_pytorch_sharded_state_dict_to_flax(UpperCamelCase__						,		UpperCamelCase__							)
   return flax_state_dict




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		):


   """simple docstring"""




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

   # layer norm
   lowercase_	:    List[Any]           =     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
   lowercase_	:    Optional[int]           =     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
   lowercase_	:    Union[str, Any]           =     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
   lowercase_	:    str           =     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
   lowercase_	:    Tuple           =     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__							):
      lowercase_	:    Optional[Any]           =     pt_tensor.transpose(2						,		3						,		1						,		0							)
      return renamed_pt_tuple_key, pt_tensor

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

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

   # old PyTorch layer norm bias
   lowercase_	:    Optional[int]           =     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
   lowercase_	:    int           =     None
   if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
      lowercase_	:    List[Any]           =     pt_tuple_key[-2] + """_g"""
   elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
      lowercase_	:    Optional[Any]           =     pt_tuple_key[-2] + """_v"""
   if name is not None:
      lowercase_	:    List[Any]           =     pt_tuple_key[:-3] + (name,)
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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

   lowercase_	:    List[str]           =     flax_model.base_model_prefix

   # use params dict if the model contains batch norm layers
   if "params" in flax_model.params:
      lowercase_	:    int           =     flax_model.params["""params"""]
   else:
      lowercase_	:    Union[str, Any]           =     flax_model.params
   lowercase_	:    List[str]           =     flatten_dict(UpperCamelCase__							)

   # add batch_stats keys,values to dict
   if "batch_stats" in flax_model.params:
      lowercase_	:    Optional[Any]           =     flatten_dict(flax_model.params["batch_stats"]							)
      random_flax_state_dict.update(UpperCamelCase__							)

   lowercase_	:    int           =     {}

   lowercase_	:    List[str]           =     (model_prefix not in flax_model_params) and (
       model_prefix in {k.split("."							)[0] for k in pt_state_dict.keys()}
   )
   lowercase_	:    List[str]           =     (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():
      lowercase_	:    Dict           =     tuple(pt_key.split("."							)							)

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

      # Correctly rename weight parameters
      lowercase_	:    str           =     rename_key_and_reshape_tensor(
          UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__							)

      # add model prefix if necessary
      lowercase_	:    str           =     (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
         lowercase_	:    str           =     (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]:
            lowercase_	:    List[str]           =     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
         lowercase_	:    Union[str, Any]           =     jnp.asarray(UpperCamelCase__							)

      else:
         # also add unexpected weight so that warning is thrown
         lowercase_	:    List[str]           =     jnp.asarray(UpperCamelCase__							)

   return unflatten_dict(UpperCamelCase__							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   import torch

   # Load the index
   lowercase_	:    List[Any]           =     {}
   for shard_file in shard_filenames:
      # load using msgpack utils
      lowercase_	:    Any           =     torch.load(UpperCamelCase__							)
      lowercase_	:    str           =     {k: v.numpy() for k, v in pt_state_dict.items()}

      lowercase_	:    List[Any]           =     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:
         lowercase_	:    str           =     flax_model.params["""params"""]

         lowercase_	:    List[str]           =     flatten_dict(UpperCamelCase__							)
         random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]							)							)
      else:
         lowercase_	:    Dict           =     flax_model.params
         lowercase_	:    List[Any]           =     flatten_dict(UpperCamelCase__							)

      lowercase_	:    Optional[Any]           =     (model_prefix not in flax_model_params) and (
          model_prefix in {k.split("."							)[0] for k in pt_state_dict.keys()}
      )
      lowercase_	:    List[Any]           =     (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():
         lowercase_	:    Tuple           =     tuple(pt_key.split("."							)							)

         # remove base model prefix if necessary
         lowercase_	:    Optional[int]           =     pt_tuple_key[0] == model_prefix
         if load_model_with_head_into_base_model and has_base_model_prefix:
            lowercase_	:    Optional[Any]           =     pt_tuple_key[1:]

         # Correctly rename weight parameters
         lowercase_	:    int           =     rename_key_and_reshape_tensor(
             UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__							)
         # add model prefix if necessary
         lowercase_	:    Union[str, Any]           =     (model_prefix,) + flax_key in random_flax_state_dict
         if load_base_model_into_model_with_head and require_base_model_prefix:
            lowercase_	:    List[Any]           =     (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]:
               lowercase_	:    Optional[int]           =     jnp.asarray(UpperCamelCase__							)
               continue
            if "var" in flax_key[-1]:
               lowercase_	:    Any           =     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
            lowercase_	:    Tuple           =     jnp.asarray(UpperCamelCase__							)

         else:
            # also add unexpected weight so that warning is thrown
            lowercase_	:    List[Any]           =     jnp.asarray(UpperCamelCase__							)
   return unflatten_dict(UpperCamelCase__							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     os.path.abspath(UpperCamelCase__							)
   logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""							)

   # import correct flax class
   lowercase_	:    List[str]           =     getattr(UpperCamelCase__						,		"Flax" + model.__class__.__name__							)

   # load flax weight dict
   with open(UpperCamelCase__						,		"rb"							) as state_f:
      try:
         lowercase_	:    Optional[int]           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """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
   lowercase_	:    List[Any]           =     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."							)
      lowercase_	:    Optional[Any]           =     jax.tree_util.tree_map(
          lambda _UpperCamelCase							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params						,		UpperCamelCase__							)

   lowercase_	:    List[Any]           =     flatten_dict(UpperCamelCase__							)
   lowercase_	:    Optional[Any]           =     pt_model.state_dict()

   lowercase_	:    int           =     (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()}
   )
   lowercase_	:    List[str]           =     (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
   lowercase_	:    Union[str, Any]           =     []
   lowercase_	:    List[str]           =     set(pt_model_dict.keys()							)

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      lowercase_	:    Dict           =     flax_key_tuple[0] == pt_model.base_model_prefix
      lowercase_	:    int           =     """.""".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:
         lowercase_	:    Optional[int]           =     flax_key_tuple[1:]
      elif load_base_model_into_model_with_head and require_base_model_prefix:
         lowercase_	:    List[str]           =     (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
         lowercase_	:    Tuple           =     flax_key_tuple[:-1] + ("""weight""",)
         lowercase_	:    Dict           =     jnp.transpose(UpperCamelCase__						,		(3, 2, 0, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__							) not in pt_model_dict:
         # linear layer
         lowercase_	:    str           =     flax_key_tuple[:-1] + ("""weight""",)
         lowercase_	:    str           =     flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
         lowercase_	:    Any           =     flax_key_tuple[:-1] + ("""weight""",)

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

      if "batch_stats" in flax_state:
         lowercase_	:    int           =     """.""".join(flax_key_tuple[1:]							)  # Remove the params/batch_stats header
      else:
         lowercase_	:    Optional[int]           =     """.""".join(UpperCamelCase__							)

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

      if flax_key in special_pt_names:
         lowercase_	:    Optional[int]           =     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
            lowercase_	:    List[Any]           =     np.asarray(UpperCamelCase__							) if not isinstance(UpperCamelCase__						,		np.ndarray							) else flax_tensor
            lowercase_	:    Tuple           =     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
   lowercase_	:    Union[str, Any]           =     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 tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, 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__ =			get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
 from transformers.models.plbart.modeling_plbart import shift_tokens_right

UpperCamelCase__ =			50003
UpperCamelCase__ =			50002


@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Optional[int]		       =    PLBartTokenizer
    __lowerCamelCase:   Any		       =    None
    __lowerCamelCase:   Dict		       =    False




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       super().setUp()

       # We have a SentencePiece fixture for testing
       lowercase_	:    Any           =     PLBartTokenizer(a  ,     language_codes="base"  ,     keep_accents=a			)
       tokenizer.save_pretrained(self.tmpdirname			)




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     PLBartTokenizer(a  ,     language_codes="base"  ,     keep_accents=a			)

       lowercase_	:    List[str]           =     tokenizer.tokenize("This is a test"			)
       self.assertListEqual(a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]			)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a			)  ,     [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,     )

       lowercase_	:    List[str]           =     tokenizer.tokenize("I was born in 92000, and this is falsé."			)
       self.assertListEqual(
           a  ,     [
               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",
               ".",
           ]  ,     )
       lowercase_	:    Union[str, Any]           =     tokenizer.convert_tokens_to_ids(a			)
       self.assertListEqual(
           a  ,     [
               value + tokenizer.fairseq_offset
               for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
           ]  ,     )

       lowercase_	:    Optional[int]           =     tokenizer.convert_ids_to_tokens(a			)
       self.assertListEqual(
           a  ,     [
               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>",
               ".",
           ]  ,     )

       lowercase_	:    Dict           =     tokenizer.vocab_size
       lowercase_	:    str           =     [tokenizer.convert_ids_to_tokens(a			) for x in range(end - 4  ,     a			)]

       self.assertListEqual(a  ,     ["__java__", "__python__", "__en_XX__", "<mask>"]			)

       lowercase_	:    int           =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
       lowercase_	:    str           =     tokenizer(a			).input_ids
       self.assertEqual(
           tokenizer.decode(a  ,     skip_special_tokens=a  ,     clean_up_tokenization_spaces=a			)  ,     a  ,     )





    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    str           =     PLBartTokenizer(a  ,     language_codes="multi"  ,     keep_accents=a			)

       lowercase_	:    List[str]           =     tokenizer.tokenize("This is a test"			)
       self.assertListEqual(a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]			)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a			)  ,     [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,     )

       lowercase_	:    Optional[Any]           =     tokenizer.tokenize("I was born in 92000, and this is falsé."			)
       self.assertListEqual(
           a  ,     [
               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",
               ".",
           ]  ,     )
       lowercase_	:    Optional[Any]           =     tokenizer.convert_tokens_to_ids(a			)
       self.assertListEqual(
           a  ,     [
               value + tokenizer.fairseq_offset
               for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
           ]  ,     )

       lowercase_	:    List[str]           =     tokenizer.convert_ids_to_tokens(a			)
       self.assertListEqual(
           a  ,     [
               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>",
               ".",
           ]  ,     )
       lowercase_	:    Dict           =     tokenizer.vocab_size
       lowercase_	:    Union[str, Any]           =     [tokenizer.convert_ids_to_tokens(a			) for x in range(end - 7  ,     a			)]

       self.assertListEqual(
           a  ,     ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]			)
       lowercase_	:    Any           =     "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
       lowercase_	:    List[Any]           =     tokenizer(a			).input_ids
       self.assertEqual(
           tokenizer.decode(a  ,     skip_special_tokens=a  ,     clean_up_tokenization_spaces=a			)  ,     a  ,     )


@require_torch
@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase						(		unittest.TestCase			):
    __lowerCamelCase:   int		       =    'uclanlp/plbart-python-en_XX'
    __lowerCamelCase:   Tuple		       =    [
        'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
        'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
    ]
    __lowerCamelCase:   List[str]		       =    [
        'Returns the maximum value of a b c.',
        'Sums the values of a b c.',
    ]
    __lowerCamelCase:   List[str]		       =    [
        134,
        5452,
        3_3460,
        3_3441,
        3_3463,
        3_3465,
        3_3463,
        3_3449,
        988,
        20,
        3_3456,
        19,
        3_3456,
        771,
        39,
        4258,
        889,
        3318,
        3_3441,
        3_3463,
        3_3465,
        3_3463,
        3_3449,
        2471,
        2,
        PYTHON_CODE,
    ]




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       str			):






       '''simple docstring'''






       lowercase_	:    PLBartTokenizer           =     PLBartTokenizer.from_pretrained(
           cls.checkpoint_name  ,     language_codes="base"  ,     src_lang="python"  ,     tgt_lang="en_XX"			)
       lowercase_	:    List[str]           =     1
       return cls




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]  ,     5_0_0_0_1			)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]  ,     5_0_0_0_2			)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]  ,     5_0_0_0_3			)




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    int           =     self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
       self.assertListEqual(self.expected_src_tokens  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       self.assertIn(a  ,     self.tokenizer.all_special_ids			)
       lowercase_	:    List[Any]           =     [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
       lowercase_	:    Optional[int]           =     self.tokenizer.decode(a  ,     skip_special_tokens=a			)
       lowercase_	:    Any           =     self.tokenizer.decode(generated_ids[1:]  ,     skip_special_tokens=a			)
       self.assertEqual(a  ,     a			)
       self.assertNotIn(self.tokenizer.eos_token  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 2_0]
       self.assertIsInstance(src_text[0]  ,     a			)
       lowercase_	:    Tuple           =     1_0
       lowercase_	:    int           =     self.tokenizer(a  ,     max_length=a  ,     truncation=a			).input_ids[0]
       self.assertEqual(ids[-2]  ,     2			)
       self.assertEqual(ids[-1]  ,     a			)
       self.assertEqual(len(a			)  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]			)  ,     [5_0_0_0_4, 5_0_0_0_1]			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     tempfile.mkdtemp()
       lowercase_	:    List[str]           =     self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(a			)
       lowercase_	:    Tuple           =     PLBartTokenizer.from_pretrained(a			)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,     a			)




    @require_torch
    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     self.tokenizer(self.src_text  ,     text_target=self.tgt_text  ,     padding=a  ,     return_tensors="pt"			)
       lowercase_	:    List[str]           =     shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id			)

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       self.assertEqual(batch.input_ids[1][-2:].tolist()  ,     [2, PYTHON_CODE]			)
       self.assertEqual(batch.decoder_input_ids[1][0]  ,     a			)
       self.assertEqual(batch.decoder_input_ids[1][-1]  ,     2			)
       self.assertEqual(batch.labels[1][-2:].tolist()  ,     [2, EN_CODE]			)




    @require_torch
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     self.tokenizer(
           self.src_text  ,     text_target=self.tgt_text  ,     padding=a  ,     truncation=a  ,     max_length=len(self.expected_src_tokens			)  ,     return_tensors="pt"  ,     )
       lowercase_	:    Dict           =     shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id			)

       self.assertIsInstance(a  ,     a			)

       self.assertEqual((2, 2_6)  ,     batch.input_ids.shape			)
       self.assertEqual((2, 2_6)  ,     batch.attention_mask.shape			)
       lowercase_	:    Dict           =     batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens  ,     a			)
       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, PYTHON_CODE]			)




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.tokenizer(self.src_text  ,     padding=a  ,     truncation=a  ,     max_length=3  ,     return_tensors="pt"			)
       lowercase_	:    List[str]           =     self.tokenizer(
           text_target=self.tgt_text  ,     padding=a  ,     truncation=a  ,     max_length=1_0  ,     return_tensors="pt"			)
       lowercase_	:    Dict           =     targets["input_ids"]
       lowercase_	:    str           =     shift_tokens_right(a  ,     self.tokenizer.pad_token_id			)

       self.assertEqual(batch.input_ids.shape[1]  ,     3			)
       self.assertEqual(batch.decoder_input_ids.shape[1]  ,     1_0			)





    @require_torch
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.tokenizer._build_translation_inputs(
           "A test"  ,     return_tensors="pt"  ,     src_lang="en_XX"  ,     tgt_lang="java"			)

       self.assertEqual(
           nested_simplify(a			)  ,     {
               # A, test, EOS, en_XX
               "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
               "attention_mask": [[1, 1, 1, 1]],
               # java
               "forced_bos_token_id": 5_0_0_0_1,
           }  ,     )




