


"""simple docstring"""


from math import ceil


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			list(range(0    , _UpperCamelCase      )      )

			__lowerCAmelCase				    =			[item for sublist in list(device_map.values()      ) for item in sublist]

			# Duplicate check
			__lowerCAmelCase				    =			[]
			for i in device_map_blocks:
						if device_map_blocks.count(_UpperCamelCase      ) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(_UpperCamelCase      )
    # Missing blocks
			__lowerCAmelCase				    =			[i for i in blocks if i not in device_map_blocks]
			__lowerCAmelCase				    =			[i for i in device_map_blocks if i not in blocks]

			if len(_UpperCamelCase      ) != 0:
						raise ValueError(
						    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
						    " These attention blocks were specified more than once: " + str(_UpperCamelCase      )      )
			if len(_UpperCamelCase      ) != 0:
						raise ValueError(
						    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
						    "blocks to a device on the device_map: " + str(_UpperCamelCase      )      )
			if len(_UpperCamelCase      ) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(_UpperCamelCase      )      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			list(range(_UpperCamelCase      )      )
			__lowerCAmelCase				    =			int(ceil(n_layers / len(_UpperCamelCase      )      )      )
			__lowerCAmelCase				    =			[layers[i : i + n_blocks] for i in range(0    , _UpperCamelCase    , _UpperCamelCase      )]

			return dict(zip(_UpperCamelCase    , _UpperCamelCase      )      )





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file']			)
  UpperCamelCase_					:						Any =					os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file']			)
  with open(self.vocab_file   ,      'w'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



