






"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 	__UpperCAmelCase		(				unittest.TestCase	):


	'''simple docstring'''





	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=tempfile.mkdtemp()

		_SCREAMING_SNAKE_CASE						=[
		    '''[UNK]''',
		    '''[CLS]''',
		    '''[SEP]''',
		    '''[PAD]''',
		    '''[MASK]''',
		    '''want''',
		    '''##want''',
		    '''##ed''',
		    '''wa''',
		    '''un''',
		    '''runn''',
		    '''##ing''',
		    ''',''',
		    '''low''',
		    '''lowest''',
		]
		_SCREAMING_SNAKE_CASE						=os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['''vocab_file''']					)
		with open(self.vocab_file      ,      '''w'''      ,      encoding='''utf-8'''					) as vocab_writer:
			vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]					)					)

		_SCREAMING_SNAKE_CASE						={
		    '''do_resize''': True,
		    '''size''': 2_0,
		    '''do_center_crop''': True,
		    '''crop_size''': 1_8,
		    '''do_normalize''': True,
		    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
		    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
		}
		_SCREAMING_SNAKE_CASE						=os.path.join(self.tmpdirname      ,      __lowerCAmelCase					)
		with open(self.image_processor_file      ,      '''w'''      ,      encoding='''utf-8'''					) as fp:
			json.dump(__lowerCAmelCase      ,      __lowerCAmelCase					)


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

		return BertTokenizer.from_pretrained(self.tmpdirname      ,      **__lowerCAmelCase					)


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

		return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **__lowerCAmelCase					)


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

		return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,      **__lowerCAmelCase					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		shutil.rmtree(self.tmpdirname					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=[np.random.randint(2_5_5      ,      size=(3, 3_0, 4_0_0)      ,      dtype=np.uinta					)]
		_SCREAMING_SNAKE_CASE						=[Image.fromarray(np.moveaxis(__lowerCAmelCase      ,      0      ,      -1					)					) for x in image_inputs]
		return image_inputs


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()
		_SCREAMING_SNAKE_CASE						=self.get_rust_tokenizer()
		_SCREAMING_SNAKE_CASE						=self.get_image_processor()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)
		processor_slow.save_pretrained(self.tmpdirname					)
		_SCREAMING_SNAKE_CASE						=AlignProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)
		processor_fast.save_pretrained(self.tmpdirname					)
		_SCREAMING_SNAKE_CASE						=AlignProcessor.from_pretrained(self.tmpdirname					)

		self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()					)
		self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()					)
		self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()					)
		self.assertIsInstance(processor_slow.tokenizer      ,      __lowerCAmelCase					)
		self.assertIsInstance(processor_fast.tokenizer      ,      __lowerCAmelCase					)

		self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()					)
		self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()					)
		self.assertIsInstance(processor_slow.image_processor      ,      __lowerCAmelCase					)
		self.assertIsInstance(processor_fast.image_processor      ,      __lowerCAmelCase					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()					)
		processor.save_pretrained(self.tmpdirname					)

		_SCREAMING_SNAKE_CASE						=self.get_tokenizer(bos_token='''(BOS)'''      ,      eos_token='''(EOS)'''					)
		_SCREAMING_SNAKE_CASE						=self.get_image_processor(do_normalize=__lowerCAmelCase      ,      padding_value=1.0					)

		_SCREAMING_SNAKE_CASE						=AlignProcessor.from_pretrained(
		    self.tmpdirname      ,      bos_token='''(BOS)'''      ,      eos_token='''(EOS)'''      ,      do_normalize=__lowerCAmelCase      ,      padding_value=1.0					)

		self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()					)
		self.assertIsInstance(processor.tokenizer      ,      __lowerCAmelCase					)

		self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()					)
		self.assertIsInstance(processor.image_processor      ,      __lowerCAmelCase					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						=self.prepare_image_inputs()

		_SCREAMING_SNAKE_CASE						=image_processor(__lowerCAmelCase      ,      return_tensors='''np'''					)
		_SCREAMING_SNAKE_CASE						=processor(images=__lowerCAmelCase      ,      return_tensors='''np'''					)

		for key in input_image_proc.keys():
			self.assertAlmostEqual(input_image_proc[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						='''lower newer'''

		_SCREAMING_SNAKE_CASE						=processor(text=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						=tokenizer(__lowerCAmelCase      ,      padding='''max_length'''      ,      max_length=6_4					)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						='''lower newer'''
		_SCREAMING_SNAKE_CASE						=self.prepare_image_inputs()

		_SCREAMING_SNAKE_CASE						=processor(text=__lowerCAmelCase      ,      images=__lowerCAmelCase					)

		self.assertListEqual(list(inputs.keys()					)      ,      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']					)

		# test if it raises when no input is passed
		with pytest.raises(__lowerCAmelCase					):
			processor()


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		_SCREAMING_SNAKE_CASE						=processor.batch_decode(__lowerCAmelCase					)
		_SCREAMING_SNAKE_CASE						=tokenizer.batch_decode(__lowerCAmelCase					)

		self.assertListEqual(__lowerCAmelCase      ,      __lowerCAmelCase					)







	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=AlignProcessor(tokenizer=__lowerCAmelCase      ,      image_processor=__lowerCAmelCase					)

		_SCREAMING_SNAKE_CASE						='''lower newer'''
		_SCREAMING_SNAKE_CASE						=self.prepare_image_inputs()

		_SCREAMING_SNAKE_CASE						=processor(text=__lowerCAmelCase      ,      images=__lowerCAmelCase					)

		self.assertListEqual(list(inputs.keys()					)      ,      processor.model_input_names					)









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Any =  "xlnet"
 a__					: Dict =  ["mems"]
 a__					: List[str] =  {
     "n_token": "vocab_size",  # Backward compatibility
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					int			,						__lowerCAmelCase					:					Dict=3_20_00			,						__lowerCAmelCase					:					List[str]=10_24			,						__lowerCAmelCase					:					Dict=24			,						__lowerCAmelCase					:					Optional[Any]=16			,						__lowerCAmelCase					:					Dict=40_96			,						__lowerCAmelCase					:					Any="gelu"			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					List[str]="bi"			,						__lowerCAmelCase					:					Dict=0.02			,						__lowerCAmelCase					:					Union[str, Any]=1E-12			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						__lowerCAmelCase					:					Optional[Any]=5_12			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Tuple=False			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Union[str, Any]=-1			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Any="last"			,						__lowerCAmelCase					:					List[Any]=True			,						__lowerCAmelCase					:					Tuple="tanh"			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					List[str]=5			,						__lowerCAmelCase					:					List[str]=1			,						__lowerCAmelCase					:					Optional[int]=2			,						**__lowerCAmelCase					:					List[str]			,						)   ->	Tuple:
    _A      =       vocab_size
    _A      =       d_model
    _A      =       n_layer
    _A      =       n_head
    if d_model % n_head != 0:
       raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''      )
    if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
          raise ValueError(
              f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''      )
    _A      =       d_model // n_head
    _A      =       ff_activation
    _A      =       d_inner
    _A      =       untie_r
    _A      =       attn_type

    _A      =       initializer_range
    _A      =       layer_norm_eps

    _A      =       dropout
    _A      =       mem_len
    _A      =       reuse_len
    _A      =       bi_data
    _A      =       clamp_len
    _A      =       same_length

    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_last_dropout
    _A      =       start_n_top
    _A      =       end_n_top

    _A      =       bos_token_id
    _A      =       pad_token_id
    _A      =       eos_token_id

    if "use_cache" in kwargs:
       warnings.warn(
           '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
           ''' instead.'''			,						__lowerCAmelCase			,						)
       _A      =       kwargs['''use_cache''']

    _A      =       use_mems_eval
    _A      =       use_mems_train
    super().__init__(pad_token_id=__lowerCAmelCase			,						bos_token_id=__lowerCAmelCase			,						eos_token_id=__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Union[str, Any]:
    logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )
    return -1

 @max_position_embeddings.setter
 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    # Message copied from Transformer-XL documentation
    raise NotImplementedError(
        f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )



