




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=30       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=0.02       ,)			->		Dict:
         UpperCAmelCase_      :							List[Any]       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							str       =      image_size
         UpperCAmelCase_      :							List[Any]       =      patch_size
         UpperCAmelCase_      :							List[str]       =      num_channels
         UpperCAmelCase_      :							Optional[int]       =      is_training
         UpperCAmelCase_      :							Any       =      use_labels
         UpperCAmelCase_      :							Any       =      hidden_size
         UpperCAmelCase_      :							Dict       =      num_hidden_layers
         UpperCAmelCase_      :							List[str]       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							Any       =      hidden_act
         UpperCAmelCase_      :							Optional[int]       =      hidden_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							int       =      type_sequence_label_size
         UpperCAmelCase_      :							List[str]       =      initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_      :							List[Any]       =      (image_size // patch_size) ** 2
         UpperCAmelCase_      :							Tuple       =      num_patches + 1





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Optional[int]       =      ViTConfig(
             image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,is_decoder=_SCREAMING_SNAKE_CASE       ,initializer_range=self.initializer_range       ,)

         return config, pixel_values





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Union[str, Any]       =      FlaxViTModel(config=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE  )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_      :							Union[str, Any]       =      (self.image_size, self.image_size)
         UpperCAmelCase_      :							Union[str, Any]       =      (self.patch_size, self.patch_size)
         UpperCAmelCase_      :							int       =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, num_patches + 1, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Optional[Any]       =      self.type_sequence_label_size
         UpperCAmelCase_      :							Optional[int]       =      FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         UpperCAmelCase_      :							Optional[int]       =      1
         UpperCAmelCase_      :							int       =      FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         UpperCAmelCase_      :							Optional[Any]       =      model(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[Any]       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Dict       =      config_and_inputs
         UpperCAmelCase_      :							str       =      {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_flax
class   __a(					_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							Any       =      FlaxViTModelTester(self  )
         UpperCAmelCase_      :							List[str]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,has_text_modality=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Optional[int]:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_      :							Optional[int]       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                   UpperCAmelCase_      :							int       =      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      model_class(_SCREAMING_SNAKE_CASE  )

                   @jax.jit
                   def model_jitted(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ):
                        return model(pixel_values=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )

                   with self.subTest('''JIT Enabled'''  ):
                        UpperCAmelCase_      :							Optional[Any]       =      model_jitted(**_SCREAMING_SNAKE_CASE  ).to_tuple()

                   with self.subTest('''JIT Disabled'''  ):
                        with jax.disable_jit():
                             UpperCAmelCase_      :							int       =      model_jitted(**_SCREAMING_SNAKE_CASE  ).to_tuple()

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,len(_SCREAMING_SNAKE_CASE  )  )
                   for jitted_output, output in zip(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                        self.assertEqual(jitted_output.shape       ,output.shape  )





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_      :							Any       =      model_class_name.from_pretrained('''google/vit-base-patch16-224'''  )
              UpperCAmelCase_      :							Union[str, Any]       =      model(np.ones((1, 3, 224, 224)  )  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __a          =			None


__a          =			logging.get_logger(__name__)


__a          =			'▁'

__a          =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a          =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__a          =			{
    'google/pegasus-xsum': 512,
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				PegasusTokenizer
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<mask_2>"       ,_SCREAMING_SNAKE_CASE="<mask_1>"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=103       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      offset

         if additional_special_tokens is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   raise TypeError(
                       f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE  )}, but is'''
                       f''' {type(_SCREAMING_SNAKE_CASE  )}'''  )

              UpperCAmelCase_      :							str       =      (
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE  )       ,self.offset - 1  )
              ]

              if len(set(_SCREAMING_SNAKE_CASE  )  ) != len(_SCREAMING_SNAKE_CASE  ):
                   raise ValueError(
                       '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                       f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
              UpperCAmelCase_      :							int       =      additional_special_tokens_extended
         else:
              UpperCAmelCase_      :							Any       =      [mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [f'''<unk_{i}>''' for i in range(2       ,self.offset  )]

         super().__init__(
             _SCREAMING_SNAKE_CASE       ,tokenizer_file=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,mask_token_sent=_SCREAMING_SNAKE_CASE       ,offset=_SCREAMING_SNAKE_CASE       ,additional_special_tokens=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							str       =      vocab_file
         UpperCAmelCase_      :							Dict       =      False if not self.vocab_file else True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Dict       =      set(self.all_special_ids  )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
              raise ValueError(
                  '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
                  f''' {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}'''  )

         return [1 if x in all_special_ids else 0 for x in seq]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:
         if already_has_special_tokens:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  )
         elif token_ids_a is None:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  ) + [1]
         else:
              return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''  )

         if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Optional[int]       =      os.path.join(
             _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(_SCREAMING_SNAKE_CASE  ):
              copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE  )

         return (out_vocab_file,)