
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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[str]=13			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Optional[int]=2			,							UpperCamelCase__			: List[Any]=3			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Optional[Any]=32			,							UpperCamelCase__			: int=5			,							UpperCamelCase__			: List[Any]=4			,							UpperCamelCase__			: List[str]=37			,							UpperCamelCase__			: Any="gelu"			,							UpperCamelCase__			: List[Any]=0.1			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: Dict=10			,							UpperCamelCase__			: List[Any]=0.02			,							)			->	Dict:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       image_size
    __magic_name__				     =       patch_size
    __magic_name__				     =       num_channels
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       type_sequence_label_size
    __magic_name__				     =       initializer_range

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (image_size // patch_size) ** 2
    __magic_name__				     =       num_patches + 1
  def 							_lowercase							( self			: Any    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

    __magic_name__				     =       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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)

    return config, pixel_values
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModel(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (self.image_size, self.image_size)
    __magic_name__				     =       (self.patch_size, self.patch_size)
    __magic_name__				     =       (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 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.type_sequence_label_size
    __magic_name__				     =       FlaxViTForImageClassification(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

    # test greyscale images
    __magic_name__				     =       1
    __magic_name__				     =       FlaxViTForImageClassification(UpperCamelCase__    )

    __magic_name__				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
    __magic_name__				     =       model(UpperCamelCase__    )





  def 							_lowercase							( self			: int    )			->	int:







    """simple docstring"""






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







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
  def 							_lowercase							( self			: Tuple    )			->	None:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: int    )			->	Dict:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 							_lowercase							( self			: Optional[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )
  def 							_lowercase							( self			: str    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       model_class(UpperCamelCase__    )

        @jax.jit
        def model_jitted(UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Dict    ):
          return model(pixel_values=UpperCamelCase__			,							**UpperCamelCase__    )

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

        with self.subTest("""JIT Disabled"""    ):
          with jax.disable_jit():
            __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

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





  @slow
  def 							_lowercase							( self			: Tuple    )			->	Any:







    """simple docstring"""






    for model_class_name in self.all_model_classes:
      __magic_name__				     =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
      __magic_name__				     =       model(np.ones((1, 3, 224, 224)    )    )
      self.assertIsNotNone(UpperCamelCase__    )



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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__A				=None


__A				=logging.get_logger(__name__)


__A				={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

__A				={
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
__A				=['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = MBartTokenizer

	lowerCAmelCase__         = []
	lowerCAmelCase__         = []



	def __init__(    self						,  lowercase=None						,  lowercase=None						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="</s>"						,  lowercase="<s>"						,  lowercase="<unk>"						,  lowercase="<pad>"						,  lowercase="<mask>"						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  **lowercase						,  )				->       Dict:
					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else mask_token

					super().__init__(
					    vocab_file=lowercase						,  tokenizer_file=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  sep_token=lowercase						,  cls_token=lowercase						,  unk_token=lowercase						,  pad_token=lowercase						,  mask_token=lowercase						,  src_lang=lowercase						,  tgt_lang=lowercase						,  additional_special_tokens=lowercase						,  **lowercase						,  )

					lowerCamelCase_						=			vocab_file
					lowerCamelCase_						=			False if not self.vocab_file else True

					lowerCamelCase_						=			FAIRSEQ_LANGUAGE_CODES.copy()

					if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

					self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
					lowerCamelCase_						=			{
					    lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					lowerCamelCase_						=			src_lang if src_lang is not None else "en_XX"
					lowerCamelCase_						=			self.convert_tokens_to_ids(self._src_lang )
					lowerCamelCase_						=			tgt_lang
					self.set_src_lang_special_tokens(self._src_lang )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self._src_lang



	@src_lang.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang )



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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       List[Any]:
					if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			self(lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  **lowercase )
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			tgt_lang_id
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = "en_XX"						,  lowercase = None						,  lowercase = "ro_RO"						,  **lowercase						,  )				->       BatchEncoding:
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			tgt_lang
					return super().prepare_seqaseq_batch(lowercase						,  lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.set_src_lang_special_tokens(self.src_lang )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = 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(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (out_vocab_file,)


