



'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				Any ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   dataset
							_UpperCamelCase					:		Optional[Any] 				=   process
							_UpperCamelCase					:		Optional[Any] 				=   params





	def __len__( self					:				Tuple ):







							'''simple docstring'''
							return len(self.dataset )





	def __getitem__( self					:				Tuple ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   self.dataset[i]
							_UpperCamelCase					:		Dict 				=   self.process(lowerCamelCase__ ,**self.params )
							return processed






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				int ,lowerCamelCase__					:				int ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				Optional[int]=None ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   loader
							_UpperCamelCase					:		Tuple 				=   infer
							_UpperCamelCase					:		List[str] 				=   params
							if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													_UpperCamelCase					:		Any 				=   None
							_UpperCamelCase					:		Union[str, Any] 				=   loader_batch_size

							# Internal bookkeeping
							_UpperCamelCase					:		Optional[Any] 				=   None
							_UpperCamelCase					:		str 				=   None





	def __len__( self					:				List[str] ):







							'''simple docstring'''
							return len(self.loader )





	def __iter__( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   iter(self.loader )
							return self





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							if isinstance(self._loader_batch_data ,torch.Tensor ):
													# Batch data is simple tensor, just fetch the slice
													_UpperCamelCase					:		Union[str, Any] 				=   self._loader_batch_data[self._loader_batch_index]
							else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													_UpperCamelCase					:		Union[str, Any] 				=   {}
													for k, element in self._loader_batch_data.items():
																			if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
																									# Convert ModelOutput to tuple first
																									_UpperCamelCase					:		str 				=   element.to_tuple()
																									if isinstance(element[0] ,torch.Tensor ):
																															_UpperCamelCase					:		Union[str, Any] 				=   tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																									elif isinstance(element[0] ,np.ndarray ):
																															_UpperCamelCase					:		str 				=   tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
																									continue
																			if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
																									# Those are stored as lists of tensors so need specific unbatching.
																									if isinstance(element[0] ,torch.Tensor ):
																															_UpperCamelCase					:		Dict 				=   tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																									elif isinstance(element[0] ,np.ndarray ):
																															_UpperCamelCase					:		Tuple 				=   tuple(np.expand_dims(el[self._loader_batch_index] ,0 ) for el in element )
																									continue
																			if element is None:
																									# This can happen for optional data that get passed around
																									_UpperCamelCase					:		Optional[int] 				=   None
																			elif isinstance(element[self._loader_batch_index] ,torch.Tensor ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers

																									_UpperCamelCase					:		int 				=   element[self._loader_batch_index].unsqueeze(0 )
																			elif isinstance(element[self._loader_batch_index] ,np.ndarray ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers
																									_UpperCamelCase					:		Optional[Any] 				=   np.expand_dims(element[self._loader_batch_index] ,0 )
																			else:
																									# This is typically a list, so no need to `unsqueeze`.
																									_UpperCamelCase					:		Union[str, Any] 				=   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													_UpperCamelCase					:		Optional[int] 				=   self._loader_batch_data.__class__(lowerCamelCase__ )
							self._loader_batch_index += 1
							return result





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													# We are currently unrolling a batch so we just need to return
													# the current item within a batch
													return self.loader_batch_item()

							# We're out of items within a batch
							_UpperCamelCase					:		Tuple 				=   next(self.iterator )
							_UpperCamelCase					:		List[str] 				=   self.infer(lowerCamelCase__ ,**self.params )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
													# Try to infer the size of the batch
													if isinstance(lowerCamelCase__ ,torch.Tensor ):
																			_UpperCamelCase					:		List[Any] 				=   processed
													else:
																			_UpperCamelCase					:		List[Any] 				=   list(processed.keys() )[0]
																			_UpperCamelCase					:		Optional[int] 				=   processed[key]
													if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
																			_UpperCamelCase					:		int 				=   len(lowerCamelCase__ )
													else:
																			_UpperCamelCase					:		List[str] 				=   first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																			# could be last batch so we can't unroll as many
																			# elements.
																			_UpperCamelCase					:		int 				=   observed_batch_size
													# Setting internal index to unwrap the batch
													_UpperCamelCase					:		Dict 				=   processed
													_UpperCamelCase					:		str 				=   0
													return self.loader_batch_item()
							else:
													# We're not unrolling batches
													return processed






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				str ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				List[Any] ,lowerCamelCase__					:				Dict ,lowerCamelCase__					:				Any=None ):







							'''simple docstring'''
							super().__init__(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )





	def __iter__( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   iter(self.loader )
							_UpperCamelCase					:		List[str] 				=   None
							return self





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							if self.subiterator is None:
													_UpperCamelCase					:		Tuple 				=   self.infer(next(self.iterator ) ,**self.params )
							try:
													# Try to return next item
													_UpperCamelCase					:		Optional[Any] 				=   next(self.subiterator )
							except StopIteration:
													# When a preprocess iterator ends, we can start lookig at the next item
													# ChunkIterator will keep feeding until ALL elements of iterator
													# all have created their subiterator and have been iterating against.
													#
													# Another way to look at it, is we're basically flattening lists of lists
													# into a single list, but with generators
													_UpperCamelCase					:		List[Any] 				=   self.infer(next(self.iterator ) ,**self.params )
													_UpperCamelCase					:		int 				=   next(self.subiterator )
							return processed






class 							lowercase__	(							lowercase   ):





	def __iter__( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   iter(self.loader )
							return self





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							_UpperCamelCase					:		Dict 				=   False
							_UpperCamelCase					:		Tuple 				=   []
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													while self._loader_batch_index < self.loader_batch_size:
																			_UpperCamelCase					:		Dict 				=   self.loader_batch_item()
																			_UpperCamelCase					:		List[str] 				=   item.pop('is_last' )
																			accumulator.append(lowerCamelCase__ )
																			if is_last:
																									return accumulator

							while not is_last:
													_UpperCamelCase					:		List[Any] 				=   self.infer(next(self.iterator ) ,**self.params )
													if self.loader_batch_size is not None:
																			if isinstance(lowerCamelCase__ ,torch.Tensor ):
																									_UpperCamelCase					:		str 				=   processed
																			else:
																									_UpperCamelCase					:		Any 				=   list(processed.keys() )[0]
																									_UpperCamelCase					:		Tuple 				=   processed[key]
																			if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
																									_UpperCamelCase					:		Dict 				=   len(lowerCamelCase__ )
																			else:
																									_UpperCamelCase					:		Tuple 				=   first_tensor.shape[0]
																			if 0 < observed_batch_size < self.loader_batch_size:
																									# could be last batch so we can't unroll as many
																									# elements.
																									_UpperCamelCase					:		Any 				=   observed_batch_size
																			_UpperCamelCase					:		List[Any] 				=   processed
																			_UpperCamelCase					:		int 				=   0
																			while self._loader_batch_index < self.loader_batch_size:
																									_UpperCamelCase					:		List[Any] 				=   self.loader_batch_item()
																									_UpperCamelCase					:		Optional[Any] 				=   item.pop('is_last' )
																									accumulator.append(lowerCamelCase__ )
																									if is_last:
																															return accumulator
													else:
																			_UpperCamelCase					:		Any 				=   processed
																			_UpperCamelCase					:		List[Any] 				=   item.pop('is_last' )
																			accumulator.append(lowerCamelCase__ )
							return accumulator






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				Tuple ,lowerCamelCase__					:				Dataset ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   dataset
							_UpperCamelCase					:		str 				=   key





	def __len__( self					:				Dict ):







							'''simple docstring'''
							return len(self.dataset )





	def __getitem__( self					:				Tuple ,lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							return self.dataset[i][self.key]






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				List[Any] ,lowerCamelCase__					:				Dataset ,lowerCamelCase__					:				str ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   dataset
							_UpperCamelCase					:		Optional[Any] 				=   keya
							_UpperCamelCase					:		str 				=   keya





	def __len__( self					:				List[Any] ):







							'''simple docstring'''
							return len(self.dataset )





	def __getitem__( self					:				List[str] ,lowerCamelCase__					:				Optional[int] ):







							'''simple docstring'''
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






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


lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')


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

lowerCamelCase											=       5_00_03
lowerCamelCase											=       5_00_02




@require_sentencepiece
@require_tokenizers
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  int      =PLBartTokenizer
      UpperCamelCase__							:  Dict      =None
      UpperCamelCase__							:  Optional[Any]      =False






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						List[Any]:
             """simple docstring"""




             super().setUp()

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






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=PLBartTokenizer(lowercase_			,   language_codes='base'			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		Optional[int]		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

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

             _lowerCamelCase       :		str		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     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',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		List[Any]		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     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]
                 ]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     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>',
                     '.',
                 ]			,   )

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

             self.assertListEqual(lowercase_			,   ['__java__', '__python__', '__en_XX__', '<mask>'] )

             _lowerCamelCase       :		Optional[Any]		='java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
             _lowerCamelCase       :		Dict		=tokenizer(lowercase_ ).input_ids
             self.assertEqual(
                 tokenizer.decode(lowercase_			,   skip_special_tokens=lowercase_			,   clean_up_tokenization_spaces=lowercase_ )			,   lowercase_			,   )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=PLBartTokenizer(lowercase_			,   language_codes='multi'			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

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

             _lowerCamelCase       :		Optional[Any]		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     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',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Dict		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     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]
                 ]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     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>',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Dict		=tokenizer.vocab_size
             _lowerCamelCase       :		Optional[int]		=[tokenizer.convert_ids_to_tokens(lowercase_ ) for x in range(end - 7			,   lowercase_ )]

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




@require_torch
@require_sentencepiece
@require_tokenizers
class A  (   unittest.TestCase     ):
      UpperCamelCase__							:  List[Any]      ='uclanlp/plbart-python-en_XX'
      UpperCamelCase__							:  List[str]      =[
          'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
          'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
      ]
      UpperCamelCase__							:  Optional[int]      =[
          'Returns the maximum value of a b c.',
          'Sums the values of a b c.',
      ]
      UpperCamelCase__							:  str      =[
          134,
          5452,
          33460,
          33441,
          33463,
          33465,
          33463,
          33449,
          988,
          20,
          33456,
          19,
          33456,
          771,
          39,
          4258,
          889,
          3318,
          33441,
          33463,
          33465,
          33463,
          33449,
          2471,
          2,
          PYTHON_CODE,
      ]






      @classmethod
      def        lowerCamelCase       (					cls						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		PLBartTokenizer		=PLBartTokenizer.from_pretrained(
                 cls.checkpoint_name			,   language_codes='base'			,   src_lang='python'			,   tgt_lang='en_XX' )
             _lowerCamelCase       :		Any		=1
             return cls






      def        lowerCamelCase       (					self						:     List[str] )					->						Any:
             """simple docstring"""




             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']			,   5_0001 )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']			,   5_0002 )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']			,   5_0003 )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Tuple:
             """simple docstring"""




             self.assertIn(lowercase_			,   self.tokenizer.all_special_ids )
             _lowerCamelCase       :		Dict		=[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
             _lowerCamelCase       :		Optional[int]		=self.tokenizer.decode(lowercase_			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		int		=self.tokenizer.decode(generated_ids[1:]			,   skip_special_tokens=lowercase_ )
             self.assertEqual(lowercase_			,   lowercase_ )
             self.assertNotIn(self.tokenizer.eos_token			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
             self.assertIsInstance(src_text[0]			,   lowercase_ )
             _lowerCamelCase       :		Tuple		=10
             _lowerCamelCase       :		Optional[int]		=self.tokenizer(lowercase_			,   max_length=lowercase_			,   truncation=lowercase_ ).input_ids[0]
             self.assertEqual(ids[-2]			,   2 )
             self.assertEqual(ids[-1]			,   lowercase_ )
             self.assertEqual(len(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__'] )			,   [5_0004, 5_0001] )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=tempfile.mkdtemp()
             _lowerCamelCase       :		Dict		=self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(lowercase_ )
             _lowerCamelCase       :		Any		=PLBartTokenizer.from_pretrained(lowercase_ )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,   lowercase_ )






      @require_torch
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=self.tokenizer(self.src_text			,   text_target=self.tgt_text			,   padding=lowercase_			,   return_tensors='pt' )
             _lowerCamelCase       :		List[Any]		=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]			,   lowercase_ )
             self.assertEqual(batch.decoder_input_ids[1][-1]			,   2 )
             self.assertEqual(batch.labels[1][-2:].tolist()			,   [2, EN_CODE] )






      @require_torch
      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.tokenizer(
                 self.src_text			,   text_target=self.tgt_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=len(self.expected_src_tokens )			,   return_tensors='pt'			,   )
             _lowerCamelCase       :		List[Any]		=shift_tokens_right(batch['labels']			,   self.tokenizer.pad_token_id )

             self.assertIsInstance(lowercase_			,   lowercase_ )

             self.assertEqual((2, 26)			,   batch.input_ids.shape )
             self.assertEqual((2, 26)			,   batch.attention_mask.shape )
             _lowerCamelCase       :		List[Any]		=batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens			,   lowercase_ )
             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						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.tokenizer(self.src_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=3			,   return_tensors='pt' )
             _lowerCamelCase       :		Dict		=self.tokenizer(
                 text_target=self.tgt_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=10			,   return_tensors='pt' )
             _lowerCamelCase       :		List[str]		=targets['input_ids']
             _lowerCamelCase       :		Optional[Any]		=shift_tokens_right(lowercase_			,   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        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.tokenizer._build_translation_inputs(
                 'A test'			,   return_tensors='pt'			,   src_lang='en_XX'			,   tgt_lang='java' )

             self.assertEqual(
                 nested_simplify(lowercase_ )			,   {
                     # A, test, EOS, en_XX
                     'input_ids': [[150, 242, 2, 5_0003]],
                     'attention_mask': [[1, 1, 1, 1]],
                     # java
                     'forced_bos_token_id': 5_0001,
                 }			,   )


