

'''simple docstring'''
from math import ceil


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Dict    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =list(range(0							,			_UpperCamelCase    )    )

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

     # Duplicate check
     _SCREAMING_SNAKE_CASE							   =[]
     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
     _SCREAMING_SNAKE_CASE							   =[i for i in blocks if i not in device_map_blocks]
     _SCREAMING_SNAKE_CASE							   =[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						:					Optional[Any]							,			_UpperCamelCase						:					str    )				->      Any:
     """simple docstring"""




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

     return dict(zip(_UpperCamelCase							,			_UpperCamelCase    )    )



'''simple docstring'''
import os
import sys
import unittest


lowerCamelCase			:				Optional[Any] 			=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:				Optional[int] 			=					os.path.join(git_repo_path, "src", "transformers")

lowerCamelCase			:				Union[str, Any] 			=					"\n{0} = None\n"

lowerCamelCase			:				Optional[Any] 			=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowerCamelCase			:				List[Any] 			=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[int]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
       self.assertIsNone(_a			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tokenizers_available():'			)
       self.assertEqual(_a							, 'tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tensorflow_text_available():'			)
       self.assertEqual(_a							, 'tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers_and_vision'			)





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('torch'							, _a			)
       self.assertIn('tensorflow_text'							, _a			)
       self.assertIn('sentencepiece_and_tokenizers'							, _a			)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertModel'							, objects['tf']			)
       self.assertIn('FlaxBertModel'							, objects['flax']			)
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertTokenizer'							, objects['tensorflow_text']			)
       self.assertIn('convert_slow_tokenizer'							, objects['sentencepiece_and_tokenizers']			)





  def 						A  (		self				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =create_dummy_object('CONSTANT'							, '\'torch\''			)
       self.assertEqual(_a							, '\nCONSTANT = None\n'			)

       _SCREAMING_SNAKE_CASE							   =create_dummy_object('function'							, '\'torch\''			)
       self.assertEqual(
           _a							, '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

       _SCREAMING_SNAKE_CASE							   ='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_object('FakeClass'							, '\'torch\''			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
       self.assertEqual(dummy_files['torch']							, _a			)
