


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=3   ,							UpperCamelCase_=32   ,							UpperCamelCase_=3   ,							UpperCamelCase_=10   ,							UpperCamelCase_=[10, 20, 30, 40]   ,							UpperCamelCase_=[1, 1, 2, 1]   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_="relu"   ,							UpperCamelCase_=3   ,							UpperCamelCase_=None   ,							):
        lowercase_       :List[str]     =       parent
        lowercase_       :Dict     =       batch_size
        lowercase_       :Optional[Any]     =       image_size
        lowercase_       :str     =       num_channels
        lowercase_       :Union[str, Any]     =       embeddings_size
        lowercase_       :Optional[int]     =       hidden_sizes
        lowercase_       :List[str]     =       depths
        lowercase_       :Tuple     =       is_training
        lowercase_       :str     =       use_labels
        lowercase_       :List[str]     =       hidden_act
        lowercase_       :int     =       num_labels
        lowercase_       :str     =       scope
        lowercase_       :List[str]     =       len(UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowercase_       :int     =       None
        if self.use_labels:
             lowercase_       :Dict     =       ids_tensor([self.batch_size]   ,							self.num_labels		)

        lowercase_       :Tuple     =       self.get_config()
        return config, pixel_values, labels
   def 		UpperCamelCase  (     self		):
        return RegNetConfig(
            num_channels=self.num_channels   ,							embeddings_size=self.embeddings_size   ,							hidden_sizes=self.hidden_sizes   ,							depths=self.depths   ,							hidden_act=self.hidden_act   ,							num_labels=self.num_labels   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       TFRegNetModel(config=UpperCamelCase_		)
        lowercase_       :Dict     =       model(UpperCamelCase_   ,							training=UpperCamelCase_		)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape   ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :List[Any]     =       self.num_labels
        lowercase_       :List[str]     =       TFRegNetForImageClassification(UpperCamelCase_		)
        lowercase_       :Any     =       model(UpperCamelCase_   ,							labels=UpperCamelCase_   ,							training=UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_       :List[Any]     =       config_and_inputs
        lowercase_       :Union[str, Any]     =       {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	List[Any]									=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
   lowercase					:	Dict									=(
       {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
       if is_tf_available()
       else {}
   )

   lowercase					:	Optional[int]									=False
   lowercase					:	int									=False
   lowercase					:	str									=False
   lowercase					:	Dict									=False
   lowercase					:	str									=False
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       TFRegNetModelTester(self		)
        lowercase_       :Tuple     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							has_text_modality=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        return
   @unittest.skip(reason='''RegNet does not use inputs_embeds'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''		)		) == 0   ,							reason='''TF does not support backprop for grouped convolutions on CPU.'''   ,							)
   @slow
   def 		UpperCamelCase  (     self		):
        super().test_keras_fit()
   @unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
   def 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :List[Any]     =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_       :Union[str, Any]     =       model_class(UpperCamelCase_		)
             lowercase_       :Optional[int]     =       inspect.signature(model.call		)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_       :Optional[int]     =       [*signature.parameters.keys()]

             lowercase_       :Dict     =       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        def check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Union[str, Any]     =       model_class(UpperCamelCase_		)
             lowercase_       :List[str]     =       model(**self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)   ,							training=UpperCamelCase_		)

             lowercase_       :Tuple     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_       :List[str]     =       self.model_tester.num_stages
             self.assertEqual(len(UpperCamelCase_		)   ,							expected_num_stages + 1		)

             # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]		)   ,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,							)

        lowercase_    ,      lowercase_       :List[Any]     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :Union[str, Any]     =       ['''basic''', '''bottleneck''']
        for model_class in self.all_model_classes:
             for layer_type in layers_type:
                  lowercase_       :str     =       layer_type
                  lowercase_       :Dict     =       True
                  check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowercase_       :Optional[int]     =       True

                  check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :int     =       self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_={}		):
             lowercase_       :str     =       model(UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							**UpperCamelCase_		)
             lowercase_       :Tuple     =       model(UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							**UpperCamelCase_		).to_tuple()

             def recursive_check(UpperCamelCase_   ,							UpperCamelCase_		):
                  if isinstance(UpperCamelCase_   ,							(List, Tuple)		):
                       for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase_   ,							UpperCamelCase_		):
                            recursive_check(UpperCamelCase_   ,							UpperCamelCase_		)
                  elif tuple_object is None:
                       return
                  else:
                       self.assertTrue(
                           all(tf.equal(UpperCamelCase_   ,							UpperCamelCase_		)		)   ,							msg=(
                               '''Tuple and dict output are not equal. Difference:'''
                               f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object		)		)}"
                           )   ,							)

                  recursive_check(UpperCamelCase_   ,							UpperCamelCase_		)

        for model_class in self.all_model_classes:
             lowercase_       :str     =       model_class(UpperCamelCase_		)

             lowercase_       :int     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
             lowercase_       :List[str]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
             check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

             lowercase_       :Dict     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
             lowercase_       :Optional[Any]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
             check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

             lowercase_       :Optional[Any]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
             lowercase_       :List[str]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
             check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							{'''output_hidden_states''': True}		)

             lowercase_       :int     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
             lowercase_       :Any     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
             check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							{'''output_hidden_states''': True}		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)




   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Optional[Any]     =       TFRegNetModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




def     UpperCamelCase    (      )					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
     return image






@require_tf
@require_vision
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @cached_property
   def 		UpperCamelCase  (     self		):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
            if is_vision_available()
            else None
        )




   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

        lowercase_       :Optional[Any]     =       self.default_image_processor
        lowercase_       :Optional[Any]     =       prepare_img()
        lowercase_       :str     =       image_processor(images=UpperCamelCase_   ,							return_tensors='''tf'''		)

        # forward pass
        lowercase_       :Any     =       model(**UpperCamelCase_   ,							training=UpperCamelCase_		)

        # verify the logits
        lowercase_       :List[Any]     =       tf.TensorShape((1, 1000)		)
        self.assertEqual(outputs.logits.shape   ,							UpperCamelCase_		)

        lowercase_       :int     =       tf.constant([-0.4180, -1.5051, -3.4836]		)

        tf.debugging.assert_near(outputs.logits[0, :3]   ,							UpperCamelCase_   ,							atol=1E-4		)





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


SCREAMING_SNAKE_CASE       :		Dict         =				get_tests_dir("fixtures/test_sentencepiece.model")


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

SCREAMING_SNAKE_CASE       :		Any         =				250_004
SCREAMING_SNAKE_CASE       :		Optional[int]         =				250_020






@require_sentencepiece
@require_tokenizers
class      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Union[str, Any]									=MBartTokenizer
   lowercase					:	Dict									=MBartTokenizerFast
   lowercase					:	Dict									=True
   lowercase					:	List[Any]									=True
   def 		UpperCamelCase  (     self		):
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_       :Optional[Any]     =       MBartTokenizer(UpperCamelCase_   ,							keep_accents=UpperCamelCase_		)
        tokenizer.save_pretrained(self.tmpdirname		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       MBartTokenizer(UpperCamelCase_   ,							keep_accents=UpperCamelCase_		)

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

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

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

        lowercase_       :Optional[Any]     =       tokenizer.convert_ids_to_tokens(UpperCamelCase_		)
        self.assertListEqual(
            UpperCamelCase_   ,							[
                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 		UpperCamelCase  (     self		):
        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

        lowercase_       :Optional[Any]     =       (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})"		):
                  lowercase_       :Optional[int]     =       self.rust_tokenizer_class.from_pretrained(UpperCamelCase_   ,							**UpperCamelCase_		)
                  lowercase_       :int     =       self.tokenizer_class.from_pretrained(UpperCamelCase_   ,							**UpperCamelCase_		)

                  lowercase_       :Optional[int]     =       tempfile.mkdtemp()

                  lowercase_       :Union[str, Any]     =       tokenizer_r.save_pretrained(UpperCamelCase_		)
                  lowercase_       :int     =       tokenizer_p.save_pretrained(UpperCamelCase_		)

                  # 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		)		)
                  lowercase_       :Union[str, Any]     =       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f		)
                  self.assertSequenceEqual(UpperCamelCase_   ,							UpperCamelCase_		)

                  # Checks everything loads correctly in the same way
                  lowercase_       :str     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Optional[Any]     =       tokenizer_p.from_pretrained(UpperCamelCase_		)

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

                  shutil.rmtree(UpperCamelCase_		)

                  # Save tokenizer rust, legacy_format=True
                  lowercase_       :int     =       tempfile.mkdtemp()

                  lowercase_       :Optional[Any]     =       tokenizer_r.save_pretrained(UpperCamelCase_   ,							legacy_format=UpperCamelCase_		)
                  lowercase_       :Any     =       tokenizer_p.save_pretrained(UpperCamelCase_		)

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

                  # Checks everything loads correctly in the same way
                  lowercase_       :Optional[int]     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Dict     =       tokenizer_p.from_pretrained(UpperCamelCase_		)

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

                  shutil.rmtree(UpperCamelCase_		)

                  # Save tokenizer rust, legacy_format=False
                  lowercase_       :Tuple     =       tempfile.mkdtemp()

                  lowercase_       :Optional[int]     =       tokenizer_r.save_pretrained(UpperCamelCase_   ,							legacy_format=UpperCamelCase_		)
                  lowercase_       :Tuple     =       tokenizer_p.save_pretrained(UpperCamelCase_		)

                  # 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
                  lowercase_       :int     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Any     =       tokenizer_p.from_pretrained(UpperCamelCase_		)

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

                  shutil.rmtree(UpperCamelCase_		)






@require_torch
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	List[Any]									="""facebook/mbart-large-en-ro"""
   lowercase					:	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.""",
   ]
   lowercase					:	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.""",
   ]
   lowercase					:	Tuple									=[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
   @classmethod
   def 		UpperCamelCase  (     cls		):
        lowercase_       :MBartTokenizer     =       MBartTokenizer.from_pretrained(
            cls.checkpoint_name   ,							src_lang='''en_XX'''   ,							tgt_lang='''ro_RO'''		)
        lowercase_       :Optional[int]     =       1
        return cls
   def 		UpperCamelCase  (     self		):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']   ,							25_0001		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']   ,							25_0004		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']   ,							25_0020		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
        self.assertListEqual(self.expected_src_tokens   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.assertIn(UpperCamelCase_   ,							self.tokenizer.all_special_ids		)
        lowercase_       :List[Any]     =       [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
        lowercase_       :str     =       self.tokenizer.decode(UpperCamelCase_   ,							skip_special_tokens=UpperCamelCase_		)
        lowercase_       :List[str]     =       self.tokenizer.decode(generated_ids[1:]   ,							skip_special_tokens=UpperCamelCase_		)
        self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
        self.assertNotIn(self.tokenizer.eos_token   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       ['''this is gunna be a long sentence ''' * 20]
        assert isinstance(src_text[0]   ,							UpperCamelCase_		)
        lowercase_       :Dict     =       10
        lowercase_       :Any     =       self.tokenizer(UpperCamelCase_   ,							max_length=UpperCamelCase_   ,							truncation=UpperCamelCase_		).input_ids[0]
        self.assertEqual(ids[-2]   ,							2		)
        self.assertEqual(ids[-1]   ,							UpperCamelCase_		)
        self.assertEqual(len(UpperCamelCase_		)   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']		)   ,							[25_0026, 25_0001]		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       tempfile.mkdtemp()
        lowercase_       :List[Any]     =       self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(UpperCamelCase_		)
        lowercase_       :Tuple     =       MBartTokenizer.from_pretrained(UpperCamelCase_		)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,							UpperCamelCase_		)
   @require_torch
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.tokenizer(self.src_text   ,							text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							return_tensors='''pt'''		)
        lowercase_       :Union[str, Any]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.tokenizer(
            self.src_text   ,							text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=len(self.expected_src_tokens		)   ,							return_tensors='''pt'''   ,							)

        lowercase_       :Tuple     =       shift_tokens_right(batch['''labels''']   ,							self.tokenizer.pad_token_id		)

        self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

        self.assertEqual((2, 14)   ,							batch.input_ids.shape		)
        self.assertEqual((2, 14)   ,							batch.attention_mask.shape		)
        lowercase_       :List[str]     =       batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens   ,							UpperCamelCase_		)
        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 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.tokenizer(self.src_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=3   ,							return_tensors='''pt'''		)
        lowercase_       :int     =       self.tokenizer(
            text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=10   ,							return_tensors='''pt'''		)
        lowercase_       :Optional[int]     =       targets['''input_ids''']
        lowercase_       :List[Any]     =       shift_tokens_right(UpperCamelCase_   ,							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 		UpperCamelCase  (     self		):
        lowercase_       :int     =       self.tokenizer._build_translation_inputs(
            '''A test'''   ,							return_tensors='''pt'''   ,							src_lang='''en_XX'''   ,							tgt_lang='''ar_AR'''		)

        self.assertEqual(
            nested_simplify(UpperCamelCase_		)   ,							{
                # A, test, EOS, en_XX
                '''input_ids''': [[62, 3034, 2, 25_0004]],
                '''attention_mask''': [[1, 1, 1, 1]],
                # ar_AR
                '''forced_bos_token_id''': 25_0001,
            }   ,							)

