


'''simple docstring'''



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    LayoutLMTokenizer
	A_				=    LayoutLMTokenizerFast
	A_				=    True
	A_				=    True




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().setUp()

						__a			:       List[Any]					    =  [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__a			:       Optional[Any]					    =  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]							)							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  'UNwant\u00E9d,running'
						__a			:       Tuple					    =  'unwanted, running'
						return input_text, output_text




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.tokenizer_class(self.vocab_file							)

						__a			:       Any					    =  tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(__a						,				['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)						,				[7, 4, 5, 10, 8, 9]							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass










"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






