

"""simple docstring"""


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class    snake_case     (    __lowercase,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  CTRLTokenizer
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  False
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  False

				def 							lowercase_					(     self							:    Dict)->       Tuple:



						'''simple docstring'''


						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__lowerCAmelCase:	Tuple   				=      ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
						__lowerCAmelCase:	List[Any]   				=      dict(zip(snake_case_					,  range(len(snake_case_))))
						__lowerCAmelCase:	Tuple   				=      ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
						__lowerCAmelCase:	Optional[int]   				=      {'''unk_token''': '''<unk>'''}

						__lowerCAmelCase:	Dict   				=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase:	str   				=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["merges_file"])
						with open(self.vocab_file					,  "w"					,  encoding="utf-8") as fp:
								fp.write(json.dumps(snake_case_) + "\n")
						with open(self.merges_file					,  "w"					,  encoding="utf-8") as fp:
								fp.write("\n".join(snake_case_))

				def 							lowercase_					(     self							:    Any					,  **UpperCamelCase__							:    Tuple)->       Optional[int]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **snake_case_)

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Tuple)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      '''adapt react readapt apt'''
						__lowerCAmelCase:	Dict   				=      '''adapt react readapt apt'''
						return input_text, output_text




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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map)
						__lowerCAmelCase:	Dict   				=      '''adapt react readapt apt'''
						__lowerCAmelCase:	int   				=      '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.tokenize(snake_case_)
						self.assertListEqual(snake_case_					,  snake_case_)

						__lowerCAmelCase:	Tuple   				=      tokens + [tokenizer.unk_token]

						__lowerCAmelCase:	List[Any]   				=      [0, 1, 2, 4, 5, 1, 0, 3, 6]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_)					,  snake_case_)





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

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

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

		if len(__SCREAMING_SNAKE_CASE			) != 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(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 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(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


