
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 	__A(				_lowerCAmelCase ):

  """simple docstring"""







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(_lowerCamelCase       ,			"""embed_dim"""						)						)
       self.parent.assertTrue(hasattr(_lowerCamelCase       ,			"""num_heads"""						)						)






class 	__A:

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=13       ,			SCREAMING_SNAKE_CASE_=64       ,			SCREAMING_SNAKE_CASE_=3       ,			SCREAMING_SNAKE_CASE_=[16, 48, 96]       ,			SCREAMING_SNAKE_CASE_=[1, 3, 6]       ,			SCREAMING_SNAKE_CASE_=[1, 2, 10]       ,			SCREAMING_SNAKE_CASE_=[7, 3, 3]       ,			SCREAMING_SNAKE_CASE_=[4, 2, 2]       ,			SCREAMING_SNAKE_CASE_=[2, 1, 1]       ,			SCREAMING_SNAKE_CASE_=[2, 2, 2]       ,			SCREAMING_SNAKE_CASE_=[False, False, True]       ,			SCREAMING_SNAKE_CASE_=[0.0, 0.0, 0.0]       ,			SCREAMING_SNAKE_CASE_=0.02       ,			SCREAMING_SNAKE_CASE_=1E-12       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=2       ,			):
       UpperCamelCase__				=				parent
       UpperCamelCase__				=				batch_size
       UpperCamelCase__				=				image_size
       UpperCamelCase__				=				patch_sizes
       UpperCamelCase__				=				patch_stride
       UpperCamelCase__				=				patch_padding
       UpperCamelCase__				=				is_training
       UpperCamelCase__				=				use_labels
       UpperCamelCase__				=				num_labels
       UpperCamelCase__				=				num_channels
       UpperCamelCase__				=				embed_dim
       UpperCamelCase__				=				num_heads
       UpperCamelCase__				=				stride_kv
       UpperCamelCase__				=				depth
       UpperCamelCase__				=				cls_token
       UpperCamelCase__				=				attention_drop_rate
       UpperCamelCase__				=				initializer_range
       UpperCamelCase__				=				layer_norm_eps







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCamelCase__				=				None
       if self.use_labels:
            # create a random int32 tensor of given shape
            UpperCamelCase__				=				ids_tensor([self.batch_size]       ,			self.num_labels						)

       UpperCamelCase__				=				self.get_config()
       return config, pixel_values, labels







  def        UpperCAmelCase_					(self						):
       return CvtConfig(
           image_size=self.image_size       ,			num_labels=self.num_labels       ,			num_channels=self.num_channels       ,			embed_dim=self.embed_dim       ,			num_heads=self.num_heads       ,			patch_sizes=self.patch_sizes       ,			patch_padding=self.patch_padding       ,			patch_stride=self.patch_stride       ,			stride_kv=self.stride_kv       ,			depth=self.depth       ,			cls_token=self.cls_token       ,			attention_drop_rate=self.attention_drop_rate       ,			initializer_range=self.initializer_range       ,			)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				TFCvtModel(config=_lowerCamelCase						)
       UpperCamelCase__				=				model(_lowerCamelCase       ,			training=_lowerCamelCase						)
       UpperCamelCase__				=				(self.image_size, self.image_size)
       UpperCamelCase__		,       UpperCamelCase__				=				image_size[0], image_size[1]
       for i in range(len(self.depth						)						):
            UpperCamelCase__				=				floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
            UpperCamelCase__				=				floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.embed_dim[-1], height, width)						)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				self.num_labels
       UpperCamelCase__				=				TFCvtForImageClassification(_lowerCamelCase						)
       UpperCamelCase__				=				model(_lowerCamelCase       ,			labels=_lowerCamelCase       ,			training=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.prepare_config_and_inputs()
       UpperCamelCase__		,       UpperCamelCase__		,       UpperCamelCase__				=				config_and_inputs
       UpperCamelCase__				=				{"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_tf
class 	__A(				_lowerCAmelCase				,      _lowerCAmelCase				,      unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__			    =			(
      {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			False
  SCREAMING_SNAKE_CASE__			    =			False







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				TFCvtModelTester(self						)
       UpperCamelCase__				=				TFCvtConfigTester(self       ,			config_class=_lowerCamelCase       ,			has_text_modality=_lowerCamelCase       ,			hidden_size=37						)







  def        UpperCAmelCase_					(self						):
       self.config_tester.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







  @unittest.skip(reason="""Cvt does not output attentions"""						)
  def        UpperCAmelCase_					(self						):
       pass







  @unittest.skip(reason="""Cvt does not use inputs_embeds"""						)
  def        UpperCAmelCase_					(self						):
       pass







  @unittest.skip(reason="""Cvt does not support input and output embeddings"""						)
  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."""       ,			)
  def        UpperCAmelCase_					(self						):
       super().test_dataset_conversion()







  @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="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""						)
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				tf.keras.mixed_precision.Policy("""mixed_float16"""						)
       tf.keras.mixed_precision.set_global_policy(_lowerCamelCase						)
       super().test_keras_fit()
       tf.keras.mixed_precision.set_global_policy("""float32"""						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__		,       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=				model_class(_lowerCamelCase						)
            UpperCamelCase__				=				inspect.signature(model.call						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__				=				[*signature.parameters.keys()]

            UpperCamelCase__				=				["""pixel_values"""]
            self.assertListEqual(arg_names[:1]       ,			_lowerCamelCase						)







  def        UpperCAmelCase_					(self						):
       def check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=				model_class(_lowerCamelCase						)

            UpperCamelCase__				=				model(**self._prepare_for_class(_lowerCamelCase       ,			_lowerCamelCase						)						)
            UpperCamelCase__				=				outputs.hidden_states

            UpperCamelCase__				=				len(self.model_tester.depth						)
            self.assertEqual(len(_lowerCamelCase						)       ,			_lowerCamelCase						)

            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]						)       ,			[
                    self.model_tester.embed_dim[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ]       ,			)

       UpperCamelCase__		,       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=				True
            check_hidden_states_output(_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase						)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCamelCase__				=				True

            check_hidden_states_output(_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase						)







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase						)







  @slow
  def        UpperCAmelCase_					(self						):
       for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=				TFCvtModel.from_pretrained(_lowerCamelCase						)
            self.assertIsNotNone(_lowerCamelCase						)






def 						__magic_name__ (    ):



     '''simple docstring'''




     UpperCamelCase__				=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     return image




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

  """simple docstring"""







  @cached_property
  def        UpperCAmelCase_					(self						):
       return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)







  @slow
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

       UpperCamelCase__				=				self.default_image_processor
       UpperCamelCase__				=				prepare_img()
       UpperCamelCase__				=				image_processor(images=_lowerCamelCase       ,			return_tensors="""tf"""						)

       # forward pass
       UpperCamelCase__				=				model(**_lowerCamelCase						)

       # verify the logits
       UpperCamelCase__				=				tf.TensorShape((1, 10_00)						)
       self.assertEqual(outputs.logits.shape       ,			_lowerCamelCase						)

       UpperCamelCase__				=				tf.constant([0.9285, 0.9015, -0.3150]						)
       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,			_lowerCamelCase       ,			atol=1E-4						)						)













"""simple docstring"""


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_				     =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = PegasusTokenizer
   A_						:	int						 = PegasusTokenizerFast
   A_						:	Optional[Any]						 = True
   A_						:	Union[str, Any]						 = True


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

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= '''</s>'''
     __A									= 1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase  ),       _lowerCamelCase  )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase  ),       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0],       '''<pad>'''  )
     self.assertEqual(vocab_keys[1],       '''</s>'''  )
     self.assertEqual(vocab_keys[-1],       '''v'''  )
     self.assertEqual(len(_lowerCamelCase  ),       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     self.assertEqual(self.get_tokenizer().vocab_size,       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
         ''' </s> <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     __A									= '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
     __A									= [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 9_61_03
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 1_03
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 10_24
     __A									= '''To ensure a smooth flow of bank resolutions.'''
     __A									= [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 1_50, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 10_24)
     assert batch.attention_mask.shape == (2, 10_24)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # fmt: off
     __A									= {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCamelCase,       model_name='''google/bigbird-pegasus-large-arxiv''',       revision='''ba85d0851d708441f91440d509690f1ab6353415''',       )





@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = PegasusTokenizer
   A_						:	Union[str, Any]						 = PegasusTokenizerFast
   A_						:	Any						 = True
   A_						:	str						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase,       offset=0,       mask_token_sent=_lowerCamelCase,       mask_token='''[MASK]'''  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 10_00, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 40_96)
     assert batch.attention_mask.shape == (2, 40_96)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= (
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A									= self._large_tokenizer(_lowerCamelCase  ).input_ids

     self.assertListEqual(
         _lowerCamelCase,       [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1],       )




